EMNLP
SPLADE-Code brings learned sparse retrieval to code search at up to 8B parameters
Researchers at NAVER LABS Europe have introduced SPLADE-Code, which they describe as the first large-scale family of learned sparse retrieval models specialized for code…
NAVER LABS Europe Trains LLM Agents to Learn From Retrieved Experience
Researchers at NAVER LABS Europe have proposed a way to combine fine-tuning and experience retrieval so that LLM agents generalize better to tasks they have not seen…
NAVER LABS Europe teaches LLMs to write text that sounds right when spoken
Researchers at NAVER LABS Europe have studied how to make large language models produce text that works well when read aloud by a Text-to-Speech system, rather than…
NAVER LABS Europe's RRK Reranker Runs 3x-18x Faster Than Smaller Models
Researchers at NAVER LABS Europe have introduced RRK, a listwise reranker that compresses documents into fixed-size embedding representations. According to the authors…
Apple study: planning first beats retrieval first for multi-tool AI agent queries
Apple researchers report that the best way for an LLM agent to find tools depends on how complex the request is. In a study on ToolBench, retrieving tools first worked…
Apple study: LLMs and humans both struggle with translation quality annotation
Apple researchers report that large language models can sometimes agree with human annotators more than humans agree with each other when judging machine translation…
Study: Patterns in Latent Reasoning Models Don't Prove How They Reason
Patterns that researchers have read as signs of internal reasoning in latent reasoning models also show up in control models, according to a new paper from NAVER LABS…
Apple study: efficient LLM steering methods often come at a steep cost to fluency
A new Apple machine learning paper finds that efficient methods for steering large language models frequently achieve their goal at a steep cost to fluency. The study…