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🔍 Daily Picks in Interpretability & Analysis of LMs

Outstanding research in interpretability and evaluation of language models, summarized

about 14 hours ago

Dual Process Learning: Controlling Use of In-Context vs. In-Weights Strategies with Weight Forgetting

Paper • 2406.00053 • Published May 28 • 1
Token Erasure as a Footprint of Implicit Vocabulary Items in LLMs

Paper • 2406.20086 • Published 4 days ago • 3
Multi-property Steering of Large Language Models with Dynamic Activation Composition

Paper • 2406.17563 • Published 8 days ago • 4
From Insights to Actions: The Impact of Interpretability and Analysis Research on NLP

Paper • 2406.12618 • Published 15 days ago • 5

AI Paper of the Day

A collection of papers that I think are interesting, one added each day

Can Large Language Models Understand Context?

Paper • 2402.00858 • Published Feb 1 • 20
OLMo: Accelerating the Science of Language Models

Paper • 2402.00838 • Published Feb 1 • 75
Self-Rewarding Language Models

Paper • 2401.10020 • Published Jan 18 • 135
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity

Paper • 2401.17072 • Published Jan 30 • 23

Rho-1: Not All Tokens Are What You Need

Paper • 2404.07965 • Published Apr 11 • 80
VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time

Paper • 2404.10667 • Published Apr 16 • 13
Instruction-tuned Language Models are Better Knowledge Learners

Paper • 2402.12847 • Published Feb 20 • 24
DoRA: Weight-Decomposed Low-Rank Adaptation

Paper • 2402.09353 • Published Feb 14 • 23

ReFT: Representation Finetuning for Language Models

Paper • 2404.03592 • Published Apr 4 • 75

ReFT: Representation Finetuning for Language Models

Paper • 2404.03592 • Published Apr 4 • 75

ReFT: Representation Finetuning for Language Models

Paper • 2404.03592 • Published Apr 4 • 75

papers-efficiency

Mixture-of-Depths: Dynamically allocating compute in transformer-based language models

Paper • 2404.02258 • Published Apr 2 • 102
ReFT: Representation Finetuning for Language Models

Paper • 2404.03592 • Published Apr 4 • 75

Papers - Fine-tuning - Report - Llama 7B and 13B

ReFT: Representation Finetuning for Language Models

Paper • 2404.03592 • Published Apr 4 • 75

Papers - Fine-tuning - ReFT

In this paper, we propose a strong alternative to PEFTs, LoReFT. LoReFT achieves strong per- formance across benchmarks from four domains while being

ReFT: Representation Finetuning for Language Models

Paper • 2404.03592 • Published Apr 4 • 75

The Unreasonable Ineffectiveness of the Deeper Layers

Paper • 2403.17887 • Published Mar 26 • 75
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models

Paper • 2404.02258 • Published Apr 2 • 102
ReFT: Representation Finetuning for Language Models

Paper • 2404.03592 • Published Apr 4 • 75
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Paper • 2404.03715 • Published Apr 4 • 58

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