activity
20182026
most citedStarCoder: may the source be with you!

200 citations · 230 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2026

Are Online Skill and Memory Modules Always Worth Their Tokens? A Budget-Constrained Study of Web Agents

Sina Hajimiri, Masih Aminbeidokhti, Jose Dolz +4

Online web agents often augment a base actor with memory, workflow, or skill modules. These modules can improve performance, but they also consume test-time tokens, a cost rarely r…

cs.LG2024

Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning

Md Rifat Arefin, Gopeshh Subbaraj, Nicolas Gontier +4

Decoder-only Transformers often struggle with complex reasoning tasks, particularly arithmetic reasoning requiring multiple sequential operations. In this work, we identify represe…

cs.CL2023★ 200 cited

StarCoder: may the source be with you!

Raymond Li, Loubna Ben Allal, Yangtian Zi +64

The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase…

cs.CL2023★ 1 cited

Language Decision Transformers with Exponential Tilt for Interactive Text Environments

Nicolas Gontier, Pau Rodriguez, Issam Laradji +2

Text-based game environments are challenging because agents must deal with long sequences of text, execute compositional actions using text and learn from sparse rewards. We addres…

cs.CL2022★ 1 cited

Does Entity Abstraction Help Generative Transformers Reason?

Nicolas Gontier, Siva Reddy, Christopher Pal

We study the utility of incorporating entity type abstractions into pre-trained Transformers and test these methods on four NLP tasks requiring different forms of logical reasoning…

cs.LG2020★ 28 cited

Measuring Systematic Generalization in Neural Proof Generation with Transformers

Nicolas Gontier, Koustuv Sinha, Siva Reddy +1

We are interested in understanding how well Transformer language models (TLMs) can perform reasoning tasks when trained on knowledge encoded in the form of natural language. We inv…

cs.CL2018

The RLLChatbot: a solution to the ConvAI challenge

Nicolas Gontier, Koustuv Sinha, Peter Henderson +4

Current conversational systems can follow simple commands and answer basic questions, but they have difficulty maintaining coherent and open-ended conversations about specific topi…