Search Results for author: Jane Dwivedi-Yu

Found 19 papers, 10 papers with code

Teaching Large Language Models to Reason with Reinforcement Learning

no code implementations7 Mar 2024 Alex Havrilla, Yuqing Du, Sharath Chandra Raparthy, Christoforos Nalmpantis, Jane Dwivedi-Yu, Maksym Zhuravinskyi, Eric Hambro, Sainbayar Sukhbaatar, Roberta Raileanu

Surprisingly, we find the sample complexity of Expert Iteration is similar to that of PPO, requiring at most on the order of $10^6$ samples to converge from a pretrained checkpoint.

reinforcement-learning

MultiContrievers: Analysis of Dense Retrieval Representations

1 code implementation24 Feb 2024 Seraphina Goldfarb-Tarrant, Pedro Rodriguez, Jane Dwivedi-Yu, Patrick Lewis

Dense retrievers compress source documents into (possibly lossy) vector representations, yet there is little analysis of what information is lost versus preserved, and how it affects downstream tasks.

Retrieval

TOOLVERIFIER: Generalization to New Tools via Self-Verification

1 code implementation21 Feb 2024 Dheeraj Mekala, Jason Weston, Jack Lanchantin, Roberta Raileanu, Maria Lomeli, Jingbo Shang, Jane Dwivedi-Yu

Teaching language models to use tools is an important milestone towards building general assistants, but remains an open problem.

Efficient Tool Use with Chain-of-Abstraction Reasoning

no code implementations30 Jan 2024 Silin Gao, Jane Dwivedi-Yu, Ping Yu, Xiaoqing Ellen Tan, Ramakanth Pasunuru, Olga Golovneva, Koustuv Sinha, Asli Celikyilmaz, Antoine Bosselut, Tianlu Wang

LLM agents trained with our method also show more efficient tool use, with inference speed being on average ~1. 4x faster than baseline tool-augmented LLMs.

Math Mathematical Reasoning +1

ROBBIE: Robust Bias Evaluation of Large Generative Language Models

no code implementations29 Nov 2023 David Esiobu, Xiaoqing Tan, Saghar Hosseini, Megan Ung, Yuchen Zhang, Jude Fernandes, Jane Dwivedi-Yu, Eleonora Presani, Adina Williams, Eric Michael Smith

In this work, our focus is two-fold: (1) Benchmarking: a comparison of 6 different prompt-based bias and toxicity metrics across 12 demographic axes and 5 families of generative LLMs.

Benchmarking Fairness

Evaluation of Faithfulness Using the Longest Supported Subsequence

no code implementations23 Aug 2023 Anirudh Mittal, Timo Schick, Mikel Artetxe, Jane Dwivedi-Yu

Our proposed metric demonstrates an 18% enhancement over the prevailing state-of-the-art metric for faithfulness on our dataset.

Question Answering

Shepherd: A Critic for Language Model Generation

1 code implementation8 Aug 2023 Tianlu Wang, Ping Yu, Xiaoqing Ellen Tan, Sean O'Brien, Ramakanth Pasunuru, Jane Dwivedi-Yu, Olga Golovneva, Luke Zettlemoyer, Maryam Fazel-Zarandi, Asli Celikyilmaz

As large language models improve, there is increasing interest in techniques that leverage these models' capabilities to refine their own outputs.

Language Modelling

Active Learning Principles for In-Context Learning with Large Language Models

no code implementations23 May 2023 Katerina Margatina, Timo Schick, Nikolaos Aletras, Jane Dwivedi-Yu

The remarkable advancements in large language models (LLMs) have significantly enhanced the performance in few-shot learning settings.

Active Learning Few-Shot Learning +1

Active Retrieval Augmented Generation

1 code implementation11 May 2023 Zhengbao Jiang, Frank F. Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, Graham Neubig

In this work, we provide a generalized view of active retrieval augmented generation, methods that actively decide when and what to retrieve across the course of the generation.

Retrieval Sentence

Learnings from Data Integration for Augmented Language Models

no code implementations10 Apr 2023 Alon Halevy, Jane Dwivedi-Yu

One of the limitations of large language models is that they do not have access to up-to-date, proprietary or personal data.

Data Integration

EditEval: An Instruction-Based Benchmark for Text Improvements

1 code implementation27 Sep 2022 Jane Dwivedi-Yu, Timo Schick, Zhengbao Jiang, Maria Lomeli, Patrick Lewis, Gautier Izacard, Edouard Grave, Sebastian Riedel, Fabio Petroni

Evaluation of text generation to date has primarily focused on content created sequentially, rather than improvements on a piece of text.

Text Generation

PEER: A Collaborative Language Model

no code implementations24 Aug 2022 Timo Schick, Jane Dwivedi-Yu, Zhengbao Jiang, Fabio Petroni, Patrick Lewis, Gautier Izacard, Qingfei You, Christoforos Nalmpantis, Edouard Grave, Sebastian Riedel

Textual content is often the output of a collaborative writing process: We start with an initial draft, ask for suggestions, and repeatedly make changes.

Language Modelling

Atlas: Few-shot Learning with Retrieval Augmented Language Models

1 code implementation5 Aug 2022 Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, Edouard Grave

Retrieval augmented models are known to excel at knowledge intensive tasks without the need for as many parameters, but it is unclear whether they work in few-shot settings.

Fact Checking Few-Shot Learning +6

Improving Wikipedia Verifiability with AI

1 code implementation8 Jul 2022 Fabio Petroni, Samuel Broscheit, Aleksandra Piktus, Patrick Lewis, Gautier Izacard, Lucas Hosseini, Jane Dwivedi-Yu, Maria Lomeli, Timo Schick, Pierre-Emmanuel Mazaré, Armand Joulin, Edouard Grave, Sebastian Riedel

Hence, maintaining and improving the quality of Wikipedia references is an important challenge and there is a pressing need for better tools to assist humans in this effort.

Citation Recommendation Fact Checking

"That's so cute!": The CARE Dataset for Affective Response Detection

no code implementations28 Jan 2022 Jane Dwivedi-Yu, Alon Y. Halevy

The CARE method is a means of leveraging the signal that is present in comments that are posted in response to a post, providing high-precision evidence about the affective response of the readers to the post without human annotation.

Emotion Recognition

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