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12 result(s) for “okapi bm25 site hotchips pp site model kb be site have apache org site have apache site:arxiv.org” · 972 ms
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The query asks the model to recall the topic of an earlier conversation about Tesla; the gold answer is anchored in one specific session about Pooja’s dig site
from the top result below
https://arxiv.org/html/2608.02613v1
A true personal agent, such as Charlie’s assistant, should only have visibility on conversations Charlie participated in or witnessed
https://arxiv.org/html/2608.03048v1
Once all chunks have been processed, the model generates the final answer conditioned on the resulting memory (Yu et al
https://arxiv.org/html/2608.02620v1
Many benchmarks relying on LLM-judges have been proposed
https://arxiv.org/html/2501.13956v1
Current approaches using RAG have focused on broad domain knowledge and largely static corpora—that is, document contents added to a corpus seldom change
https://arxiv.org/list/cs.IR/new
IR)
Large language models (LLMs) have emerged as promising listwise rerankers for recommender systems, but their reliability under equivalent candidate permutations remains unclear
https://arxiv.org/list/cs.CL/new
Recent studies have therefore examined how to automatically derive optimization problems from natural-language descriptions, but existing benchmarks focus on settings where objectives and constraints can be written explicitly as mathematical expressions
https://arxiv.org/html/2407.16741v3
edu
Abstract
Software is one of the most powerful tools that we humans have at our disposal; it allows a skilled programmer to interact with the world in complex and profound ways
https://arxiv.org/list/cs.AI/new
CL)
Recent studies have identified an ``Artificial Hivemind'' effect in Large Language Models (LLMs) causing models to converge on a narrow, homogenized consensus even for open questions
https://arxiv.org/abs/2608.03860
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy
https://arxiv.org/abs/2608.03883
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy
https://arxiv.org/abs/2104.08663
Benchmark for Zero-shot Evaluation of Information Retrieval Models, by Nandan Thakur and 4 other authors
View PDF HTML (experimental)
Abstract:Existing neural information retrieval (IR) models have often been studied in homogeneous and narrow settings, which has considerably limited insights into their out-of
https://arxiv.org/list/cs.IR/recent
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Information Retrieval
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Wed, 5 Aug 2026