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Found 17 papers, 15 papers with code
SWE-smith: Scaling Data for Software Engineering Agents
We open source SWE-smith (collection procedure, task instances, trajectories, models) to lower the barrier of entry for research in LM systems for automated software engineering.
Metadata Conditioning Accelerates Language Model Pre-training
The vast diversity of styles, domains, and quality levels present in language model pre-training corpora is essential in developing general model capabilities, but efficiently learning and deploying the correct behaviors exemplified in each of these heterogeneous data sources is challenging.
Establishing Task Scaling Laws via Compute-Efficient Model Ladders
We develop task scaling laws and model ladders to predict the individual task performance of pretrained language models (LMs) in the overtrained setting.
How to Train Long-Context Language Models (Effectively)
We study continued training and supervised fine-tuning (SFT) of a language model (LM) to make effective use of long-context information.
OLMoE: Open Mixture-of-Experts Language Models
We introduce OLMoE, a fully open, state-of-the-art language model leveraging sparse Mixture-of-Experts (MoE).
Finding Transformer Circuits with Edge Pruning
Our method finds circuits in GPT-2 that use less than half the number of edges compared to circuits found by previous methods while being equally faithful to the full model predictions on standard circuit-finding tasks.
SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering
We investigate how interface design affects the performance of language model agents.
Ranked #3 on
Bug fixing
on SWE-bench-lite
QuRating: Selecting High-Quality Data for Training Language Models
We train a QuRater model to learn scalar ratings from pairwise judgments, and use it to annotate a 260B training corpus with quality ratings for each of the four criteria.
Poisoning Retrieval Corpora by Injecting Adversarial Passages
Dense retrievers have achieved state-of-the-art performance in various information retrieval tasks, but to what extent can they be safely deployed in real-world applications?
SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models.
Learning Transformer Programs
Recent research in mechanistic interpretability has attempted to reverse-engineer Transformer models by carefully inspecting network weights and activations.
Adapting Language Models to Compress Contexts
Transformer-based language models (LMs) are powerful and widely-applicable tools, but their usefulness is constrained by a finite context window and the expensive computational cost of processing long text documents.
Finding Dataset Shortcuts with Grammar Induction
Many NLP datasets have been found to contain shortcuts: simple decision rules that achieve surprisingly high accuracy.
A Kernel-Based View of Language Model Fine-Tuning
It has become standard to solve NLP tasks by fine-tuning pre-trained language models (LMs), especially in low-data settings.
Should You Mask 15% in Masked Language Modeling?
In this work, we revisit this important choice of MLM pre-training.
Phrase Retrieval Learns Passage Retrieval, Too
Dense retrieval methods have shown great promise over sparse retrieval methods in a range of NLP problems.
