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20162024
most citedHow Does In-Context Learning Help Prompt Tuning?

10 citations · 46 across the 13 of their papers we have counts for

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11 papers · 1 filter

cs.CL2024

Interactive Topic Models with Optimal Transport

Garima Dhanania, Sheshera Mysore, Chau Minh Pham +3

Topic models are widely used to analyze document collections. While they are valuable for discovering latent topics in a corpus when analysts are unfamiliar with the corpus, analys…

cs.CL2024

VERISCORE: Evaluating the factuality of verifiable claims in long-form text generation

Yixiao Song, Yekyung Kim, Mohit Iyyer

Existing metrics for evaluating the factuality of long-form text, such as FACTSCORE (Min et al., 2023) and SAFE (Wei et al., 2024), decompose an input text into "atomic claims" and…

cs.CL20232 cited

GEE! Grammar Error Explanation with Large Language Models

Yixiao Song, Kalpesh Krishna, Rajesh Bhatt +2

Grammatical error correction tools are effective at correcting grammatical errors in users' input sentences but do not provide users with \textit{natural language} explanations abo…

cs.CL2023

Exploring the impact of low-rank adaptation on the performance, efficiency, and regularization of RLHF

Simeng Sun, Dhawal Gupta, Mohit Iyyer

During the last stage of RLHF, a large language model is aligned to human intents via PPO training, a process that generally requires large-scale computational resources. In this t…

cs.CL20234 cited

A Critical Evaluation of Evaluations for Long-form Question Answering

Fangyuan Xu, Yixiao Song, Mohit Iyyer +1

Long-form question answering (LFQA) enables answering a wide range of questions, but its flexibility poses enormous challenges for evaluation. We perform the first targeted study o…

cs.CL2023

KNN-LM Does Not Improve Open-ended Text Generation

Shufan Wang, Yixiao Song, Andrew Drozdov +3

In this paper, we study the generation quality of interpolation-based retrieval-augmented language models (LMs). These methods, best exemplified by the KNN-LM, interpolate the LM's…