1 citations · 1 across the 5 of their papers we have counts for
5 papers
ReSIM: Re-ranking Binary Similarity Embeddings to Improve Function Search Performance
Gianluca Capozzi, Anna Paola Giancaspro, Fabio Petroni +2
Binary Function Similarity (BFS), the problem of determining whether two binary functions originate from the same source code, has been extensively studied in recent research acros…
ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders
Ofer Meshi, Krisztian Balog, Sally Goldman +5
The promise of LLM-based user simulators to improve conversational AI is hindered by a critical "realism gap," leading to systems that are optimized for simulated interactions, but…
Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation
Song Wang, Zihan Chen, Peng Wang +5
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources to address their limitations in accessing up-to-date or special…
Beyond RAG: Task-Aware KV Cache Compression for Comprehensive Knowledge Reasoning
Giulio Corallo, Orion Weller, Fabio Petroni +1
Incorporating external knowledge in large language models (LLMs) enhances their utility across diverse applications, but existing methods have trade-offs. Retrieval-Augmented Gener…
"Sorry, Come Again?" Prompting -- Enhancing Comprehension and Diminishing Hallucination with [PAUSE]-injected Optimal Paraphrasing
Vipula Rawte, S. M Towhidul Islam Tonmoy, S M Mehedi Zaman +4
Hallucination has emerged as the most vulnerable aspect of contemporary Large Language Models (LLMs). In this paper, we introduce the Sorry, Come Again (SCA) prompting, aimed to av…