semantic-retrieval

This paper proposes token-efficient retrieval methods for LLM analysis of transactional legal documents, demonstrating that structured retrieval can match full-corpus injection in answer quality while using significantly fewer tokens and lower cost.

RAGless is a semantic retrieval system that matches user questions to pre-generated question variants for closed-domain FAQ, eliminating the LLM generation step in standard RAG for improved precision.

CompressKV proposes a semantic-retrieval-guided KV-cache compression method for GQA-based LLMs, identifying Semantic Retrieval Heads to retain critical tokens. It achieves over 97% full-cache performance using only 3% of the KV cache on LongBench tasks.

This paper presents a human-in-the-loop pipeline for measuring a computer science program's coverage of curricular guidelines, applied longitudinally to CS2013 and CS2023. The framework reveals near-constant topical coverage but a gap in cognitive depth due to the newer guideline's raised expectations.

Introduces IdioLink, a retrieval benchmark of 10,700 documents and 2,140 queries across 107 idioms that tests whether models can link idiomatic expressions to conceptually equivalent literal or paraphrased meanings. Evaluations show current embedding models struggle with this task, highlighting gaps in idiom-aware semantic retrieval.

This paper proposes JSPG, a dynamic dictionary filtering framework that jointly leverages semantic, pinyin, and glyph features to improve keyword retrieval accuracy for Chinese contextual ASR, addressing the issue of homophonic errors that degrade semantic retrieval.