5 papers · 1 filter
Answer First, Reason Later: When Commitment Order Costs Accuracy in Diffusion Language Models
Jewon Yeom, Jaewon Sok, Seonghyeon Park +3
Masked diffusion language models revise many masked output positions in parallel. We call a token committed once it becomes visible and is never masked again, and call a response a…
Hallucination as Commitment Failure: Larger LLMs Misfire Despite Knowing the Answer
Jewon Yeom, Jaewon Sok, Heejun Kim +3
Hallucination is often viewed as a direct consequence of missing knowledge: a model answers incorrectly when the correct answer is absent from its generation-time distribution, and…
Garbage Attention in Large Language Models: BOS Sink Heads and Sink-aware Pruning
Jaewon Sok, Jewon Yeom, Seonghyeon Park +2
Large Language Models (LLMs) are known to contain significant redundancy, yet a systematic explanation for why certain components, particularly in higher layers, are more redundant…
EpiCaR: Knowing What You Don't Know Matters for Better Reasoning in LLMs
Jewon Yeom, Jaewon Sok, Seonghyeon Park +2
Improving the reasoning abilities of large language models (LLMs) has largely relied on iterative self-training with model-generated data. While effective at boosting accuracy, exi…
"Well, Keep Thinking": Enhancing LLM Reasoning with Adaptive Injection Decoding
Hyunbin Jin, Je Won Yeom, Seunghyun Bae +1
Large language models (LLMs) exhibit strong reasoning abilities, often attributed to few-shot or zero-shot chain-of-thought (CoT) prompting. While effective, these methods require…