arXiv AI
COFFEQA: Counterfactual-Guided Few-Shot Distillation for LLM Question Answering
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CROP: Task Relevance via Counterfactuals for Selective On-Policy Distillation
On-policy distillation (OPD) supervises a student language model on trajectories sampled from its current policy, but assigns equal credit to response tokens with unequal supervision value. Selective OPD addresses this limitation by allocating supervision non-uniformly across response tokens according to their estimated training value.
arXiv Computation and Language
Sep 17
CROP: Task Relevance via Counterfactuals for Selective On-Policy Distillation
The paper introduces CROP, a method for selective on‑policy distillation that prioritizes token‑level supervision based on task relevance. CROP uses paraphrase‑calibrated counterfactual sensitivity to measure how much each response token depends on the semantic content of the input, constructing validated original‑paraphrase‑counterfactual triplets for each prompt. Experiments in two teacher‑student settings show that CROP outperforms other selectors, improving aggregate performance by 1.92 and 2.96 points.
arXiv Computation and Language
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Learning Evidence Sufficiency Boundaries for Selective Answering in Grounded Multi-Hop QA
The paper introduces Evidence Sufficiency Boundary Training, a framework that teaches models to abstain from answering until the supplied evidence is fully sufficient, and to remain stable when additional redundant evidence is added. By constructing ordered evidence chains from datasets such as HotpotQA, 2WikiMultiHopQA, and MuSiQue, the method applies level supervision, a boundary flip margin, post‑boundary stability, and answer recall protection. Experiments with Qwen2.5‑3B‑Instruct and LoRA adaptation show improved boundary localization (flip accuracy 0.807 vs 0.781) and a lower unsupported‑answer rate (0.095 vs 0.101) while maintaining competitive raw QA F1.
Hugging Face Trending Papers
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Learning Evidence Sufficiency Boundaries for Selective Answering in Grounded Multi-Hop QA
Grounded question answering systems should answer only when the supplied evidence supports the answer. In multi-hop QA, this requirement is difficult because partial evidence can make an unsupported a...
arXiv Machine Learning
Oct 7
Rethinking Faithfulness in LLMs: A Pairwise Context-Sensitive Perspective
arXiv:2610.07894v1 Announce Type: cross Abstract: Large language models (LLMs) are expected to answer questions faithfully based on the provided context, abstaining when the context information is in...
arXiv AI
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When and How Should an Agent Clarify? CIGAsk: Teaching LLMs to Clarify via Counterfactual Information Gain
arXiv:2609.24290v1 Announce Type: new Abstract: Instruction-tuned LLMs faced with underspecified queries often commit to a single interpretation rather than ask for clarification, producing confident...