47 citations · 125 across the 23 of their papers we have counts for
9 papers · 1 filter
Contrastive Learning to Improve Retrieval for Real-world Fact Checking
Aniruddh Sriram, Fangyuan Xu, Eunsol Choi +1
Recent work on fact-checking addresses a realistic setting where models incorporate evidence retrieved from the web to decide the veracity of claims. A bottleneck in this pipeline…
To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning
Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez +7
Chain-of-thought (CoT) via prompting is the de facto method for eliciting reasoning capabilities from large language models (LLMs). But for what kinds of tasks is this extra ``thin…
CodeUpdateArena: Benchmarking Knowledge Editing on API Updates
Zeyu Leo Liu, Shrey Pandit, Xi Ye +2
Large language models (LLMs) are increasingly being used to synthesize and reason about source code. However, the static nature of these models' knowledge does not reflect the fact…
Learning to Refine with Fine-Grained Natural Language Feedback
Manya Wadhwa, Xinyu Zhao, Junyi Jessy Li +1
Recent work has explored the capability of large language models (LLMs) to identify and correct errors in LLM-generated responses. These refinement approaches frequently evaluate w…
From Distributional to Overton Pluralism: Investigating Large Language Model Alignment
Thom Lake, Eunsol Choi, Greg Durrett
The alignment process changes several properties of a large language model's (LLM's) output distribution. We analyze two aspects of post-alignment distributional shift of LLM respo…
LoFiT: Localized Fine-tuning on LLM Representations
Fangcong Yin, Xi Ye, Greg Durrett
Recent work in interpretability shows that large language models (LLMs) can be adapted for new tasks in a learning-free way: it is possible to intervene on LLM representations to e…