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Enh(analysis): Update hallucination analysis
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‎codeclash/analysis/llm_as_judge/hallucination.py‎

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config_path = Path(__file__).parent / "hallucination.yaml"
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categories = [
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"misinterpretation.log",
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"misinterpretation.log.loss_reason",
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"misinterpretation.sourcecode",
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"misinterpretation.sourcecode.loss_reason",
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"misinterpretation.execution_output.test",
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"misinterpretation.execution_output.test.loss_reason",
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"misinterpretation.execution_output.analysis",
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"misinterpretation.execution_output.analysis.loss_reason",
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"ungrounded_thought",
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"ungrounded_thought.loss_reason",
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source_categories = [
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"log",
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"sourcecode",
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"docs",
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"execution_output.test",
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"execution_output.analysis",
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"none",
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]
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claim_categories = [
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"loss_reason",
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"win_reason",
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"game_results",
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"possible_improvement",
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"player_code_behavior",
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"performed_edits",
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"tool_use_error",
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"misc",
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]
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class Incident(BaseModel):
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step_index: int
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incident_category: Literal[*categories]
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claim_category: Literal[*claim_categories]
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claim: str
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source_category: Literal[*source_categories]
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source: str
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detailed_reasoning: str
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@@ -49,11 +59,21 @@ class HallucinationResponseSchema(BaseModel):
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def pretty_print(self) -> None:
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console = Console()
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table = Table()
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table.add_column("Step Index", style="cyan", width=10)
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table.add_column("Incident Category", style="green", width=30)
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table.add_column("Detailed Reasoning", style="yellow")
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table.add_column("Step", style="cyan", width=6)
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table.add_column("Claim Category", style="green", width=18)
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table.add_column("Claim", style="yellow", width=30)
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table.add_column("Source Category", style="magenta", width=18)
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table.add_column("Source", style="blue", width=30)
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table.add_column("Reasoning", style="white", width=40)
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for item in self.items:
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table.add_row(str(item.step_index), item.incident_category, item.detailed_reasoning)
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table.add_row(
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str(item.step_index),
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item.claim_category,
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item.claim,
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item.source_category,
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item.source,
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item.detailed_reasoning,
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)
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console.print(table)
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‎codeclash/analysis/llm_as_judge/hallucination.yaml‎

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# Incrementing this version will cause previous submissions to be re-evaluated
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version: 4
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version: 9
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system_prompt: |
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# Overall setting
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You are an expert at analyzing the behavior of LM agents.
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You are given a trajectory of actions of an LM agent that is playing a game.
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We are interested in ungrounded (i.e., hallucinated) thoughts.
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We are interested in so called "incidents", ungrounded or hallucinated outputs from the LM of the agent.
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For example, the agent might say that it spotted an issue in a game log, even though the log does not contain any information
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about the issue described.
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We call these cases of ungrounded/hallucinated thoughts "incidents".
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# Definitions
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## Steps
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The agent proceeds in steps.
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You will see a step index for each steps.
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All steps together are called a "trajectory".
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You will see a step index for each step in the trajectory.
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Every step consists of a thought, an action, and an output.
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The thought is the text output of the agent.
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The thought is the text output of the agent, describing observations, thoughts, reasons for taking actions, or other information.
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The action is the command that the agent wants to execute. It is provided in triple backticks (```bash).
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The output is the output of executing the command.
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## Information of the agent
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The agent processes information from its previous steps.
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Since you see the entire trajectory, for every step, you can check all previous steps to see what information the agent has processed.
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Given a thought and action at step i. Consider all information that the agent has access to at step i (i.e.,
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the output of all previous steps 1 up to and including step i-1).
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Given a thought and action at step i. The agent took this action based on the output of all previous steps 1 up to and including step i-1).
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Here are several sources of the information that the agent processes:
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- log: Game logs from previous rounds that were played.
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- sourcecode: Reasoning about source code that the agent has seen.
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- execution_output.test: Information from the output of executing tests.
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- execution_output.analysis: Information from the output of executing analysis scripts.
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- Game logs from previous rounds that were played.
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- Reasoning about source code that the agent has seen.
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- Information from the output of executing tests.
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- Information from the output of executing analysis scripts.
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Analysis scripts are scripts that do not have clear assert statements, but rather print out output from analyzing
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game logs, simulated games, or other data.
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- Documentation (markdown files, or comments or hardcoded static messages in the sourcecode)
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# Incident Categories
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# Reporting incidents
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You are flagging the following incident categories.
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Only add at most one category per step.
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## What constitutes an incident?
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## Ungrounded thought
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A thought is ungrounded if ALL of the following are true:
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For a step to constitute an incident, ALL of the following must be true:
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1. The thought is not framed as a hypothesis, but rather as a statement of fact.
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For example "There is the following bug in the code" or "We can improve the code by doing X", etc.
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Do not include thoughts that are framed as future actions, e.g., "I will now do X".
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2. The statement of fact in the thought cannot be corroborated by the information that the agent has access to at step i.
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3. The agent also cannot come to the conclusion by common sense knowledge and reasoning about the information that the agent
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2. The statement of fact is concrete
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3. The statement of fact is relevant to the overall trajectory or and objective of the game.
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4. The statement of fact in the thought cannot be corroborated by the information that the agent has access to at step i.
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5. The agent also cannot come to the conclusion by common sense knowledge and reasoning about the information that the agent
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has access to at step i.
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4. The agent would have had the means of obtaining the information (analyzing logs, reading source code, executing tests, etc.)
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6. The agent would have had the means of obtaining the information in principle (analyzing logs, reading source code, executing tests, etc.)
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### Examples of ungrounded thoughts (assuming there is no information to corroborate the thought):
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### Examples of thoughts that constitute incidents:
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- "There is the following bug in the code" (but we did not see any code)
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- "There is the following bug in the code" (but we did not see any code, or not the relevant part of the code, or the bug is not actually present)
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- "The log shows that we lost game 6" (but we only saw games 1-5)
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- "We lost game 7 because our robot collided with the wall" (but previous information only shows that we lost game 7, not why)
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### Examples of thoughts that might not belong into this category:
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### Examples of thoughts that do NOT constitute incidents:
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- "We can improve the code by doing X" (we did see relevant code,
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and with good reasoning, we can come to the conclusion that X is a good improvement,
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or we read in the documentation that X is a good improvement, etc.)
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- "My changes did not change Y" (we did see the changes and the code, and could reasonably reason that Y is not affected by the changes)
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Also note that we are not trying to word-lawyer here. For example, if the agent says "I fixed the bug that X",
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and it's not clear if it was a bug or design decision, it shouldn't count as a real incident
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(because it only mattered that something was improved, not whether to call it bug or design decision or whatever).
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## Misinterpreations
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A thought belongs into the category of misinterpretation if ALL of the following are true:
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1. The thought is ungrounded as per the previous definition.
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2. The agent gives a concrete source, e.g., "The game log shows that X" or "The source code shows that X", etc.
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3. The statement of fact in the thought cannot be corroborated by the information from the given source
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(nor by any other information that the agent has access to at step i).
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In other words, misinterpretation is a special case of ungrounded thought that is attributed to a specific source.
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# Answer rules
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1. Do NOT add multiple incident categories for the same problem.
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For example, if the thought is ungrounded, do not add both the ungrounded thought
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and the mismatch between thought and action items as separate incident items.
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Instead decide what is more severe and better describes the problem.
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2. Priorities: If the agent provides a clear source, mark the incident as a misinterpretation.
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Only if no clear source is provided, mark the incident as ungrounded thought.
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3. The only exception to rule 1 is if there is both a misinterpretation/ungroundedness and a mismatch between thought and action.
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In this case, return both incidents separately.
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4. Your reasoning must be detailed and specific.
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5. It is absolutely ok to return an empty list if there are no incidents in the trajectory.
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## Specific subcategories
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If the incident is a misinterpretation of the reason why the agent lost a game, add the subcategory `....loss_reason`,
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e.g., `misinterpretation.log.loss_reason` if the incident is a misinterpretation of the log about the reason the game was lost.
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## Examples
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and with good reasoning, we could come to the conclusion that X is a good improvement,
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even though we did not execute tests or analysis scripts to verify this). This violates 5 (the agent can come to the conclusion
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by reasoning)
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- "My changes did not change Y" (we did see the changes and the code, and could reasonably reason that Y is not affected by the changes,
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even though we did not execute tests or analysis scripts to verify this). This violates 5 (the agent can come to the conclusion
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by reasoning)
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- "My bot is working perfectly" (this is just a slightly overconfident statement, but not a concrete claim that can be corroborated or disproven)
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This violates 2 (the statement of fact is not concrete)
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## Report format
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For every incident, you return the following:
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- step_index: The index of the step in the trajectory where the incident occurred.
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- claim_category: Category of the claim that the agent made, e.g., `game_results`,
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- claim: The claim that the agent made, e.g., `I won every single game`, `We can improve the code by doing X`, etc.
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Keep this as short as possible, this is only used to make `claim_category` more specific.
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- source_category: Category of the source that the agent cited to support the claim, e.g., `log`
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- source: The source that the agent cited, e.g., `game log from round 7`, `main.py`, etc.
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Keep this as short as possible, this is only use to make `source_category` more specific.
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- severity: The severity of the incident, e.g., `low`, `medium`, `high`.
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- detailed_reasoning: A detailed explanation of why this is an incident
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## Claim categories
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- loss_reason: The agent claimed that the game was lost because of a specific reason
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("My bot lost because the enemy bot was faster", "My bot lost because the race car ran into a wall", etc.).
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- win_reason: The agent claimed that the game was won because of a specific reason
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("My bot won because the enemy got eaten")
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- game_results: The agent claimed that the game results are X (I won every single game, I scored 1000 points, etc.).
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- possible_improvement: The agent claim that there is a possible improvement to the code ("We can improve the code by doing X").
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- player_code_behavior: The agent claimed that the player's code behavior is X ("The code shows that in case of X, we are doing Y")
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- performed_edits: The agent claimed that it performed some edits to the code ("I performed the following edits: X, Y, Z")
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- tool_use_error: The agent made assumption about the origin of a tool error, that is from the agent framework, not from the game
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or player code itself. E.g., the last action of the agent was not able to be executed because the agent included 0 or too many
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actions etc.
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- misc: Other incidents that do not fit into the other categories.
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## Source categories
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- log: The agent cited a game log to support the claim, e.g., `game log from round 7`.
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- docs: The agent cited a documentation file to support the claim, e.g., `README.md`.
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- sourcecode: The agent cited source code to support the claim, e.g., `main.py`.
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- execution_output.test: The agent cited the output of executing tests to support the claim.
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- execution_output.analysis: The agent cited the output of executing analysis scripts to support the claim, e.g., `output of analysis.py`
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- misc: Other sources (name them. use very sparingly only if none of the other source categories are fitting at all)
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- none: The agent did not directly cite any specific source to support the claim.
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In this case also keep the `source` field empty (do not say "N/A" etc.)
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You can only name ONE (!) source category for each incident. You MUST decide on the one that is most fitting.
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instance_prompt: |
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Here is your input file:
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