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🧠 AI🟢 Bullish
When Silence Is Golden: Can LLMs Learn to Abstain in Temporal QA and Beyond?
🤖AI Summary
Researchers developed a new training method combining Chain-of-Thought supervision with reinforcement learning to teach large language models when to abstain from answering temporal questions they're uncertain about. Their approach enabled a smaller Qwen2.5-1.5B model to outperform GPT-4o on temporal question answering tasks while improving reliability by 20% on unanswerable questions.
Key Takeaways
- →LLMs often produce misleading answers instead of admitting uncertainty, particularly in temporal reasoning tasks.
- →A new training pipeline combining Chain-of-Thought supervision with reinforcement learning teaches models when to abstain from answering.
- →The method enabled Qwen2.5-1.5B to surpass GPT-4o performance by 3.46% and 5.80% on TimeQA benchmarks.
- →Reinforcement learning improved reasoning accuracy but supervised fine-tuning led to overconfidence issues.
- →Implicit reasoning cues provided limited benefits compared to explicit Chain-of-Thought supervision for abstention training.
Mentioned in AI
Models
GPT-4OpenAI
#llm#temporal-reasoning#abstention#reinforcement-learning#chain-of-thought#qwen#gpt-4o#reliability#uncertainty#question-answering
Read Original →via arXiv – CS AI
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