651 research outputs found
Query Understanding in the Age of Large Language Models
Querying, conversing, and controlling search and information-seeking
interfaces using natural language are fast becoming ubiquitous with the rise
and adoption of large-language models (LLM). In this position paper, we
describe a generic framework for interactive query-rewriting using LLMs. Our
proposal aims to unfold new opportunities for improved and transparent intent
understanding while building high-performance retrieval systems using LLMs. A
key aspect of our framework is the ability of the rewriter to fully specify the
machine intent by the search engine in natural language that can be further
refined, controlled, and edited before the final retrieval phase. The ability
to present, interact, and reason over the underlying machine intent in natural
language has profound implications on transparency, ranking performance, and a
departure from the traditional way in which supervised signals were collected
for understanding intents. We detail the concept, backed by initial
experiments, along with open questions for this interactive query understanding
framework.Comment: Accepted to GENIR(SIGIR'23
Large Language Models for Information Retrieval: A Survey
As a primary means of information acquisition, information retrieval (IR)
systems, such as search engines, have integrated themselves into our daily
lives. These systems also serve as components of dialogue, question-answering,
and recommender systems. The trajectory of IR has evolved dynamically from its
origins in term-based methods to its integration with advanced neural models.
While the neural models excel at capturing complex contextual signals and
semantic nuances, thereby reshaping the IR landscape, they still face
challenges such as data scarcity, interpretability, and the generation of
contextually plausible yet potentially inaccurate responses. This evolution
requires a combination of both traditional methods (such as term-based sparse
retrieval methods with rapid response) and modern neural architectures (such as
language models with powerful language understanding capacity). Meanwhile, the
emergence of large language models (LLMs), typified by ChatGPT and GPT-4, has
revolutionized natural language processing due to their remarkable language
understanding, generation, generalization, and reasoning abilities.
Consequently, recent research has sought to leverage LLMs to improve IR
systems. Given the rapid evolution of this research trajectory, it is necessary
to consolidate existing methodologies and provide nuanced insights through a
comprehensive overview. In this survey, we delve into the confluence of LLMs
and IR systems, including crucial aspects such as query rewriters, retrievers,
rerankers, and readers. Additionally, we explore promising directions within
this expanding field
Zero-shot Query Reformulation for Conversational Search
As the popularity of voice assistants continues to surge, conversational
search has gained increased attention in Information Retrieval. However, data
sparsity issues in conversational search significantly hinder the progress of
supervised conversational search methods. Consequently, researchers are
focusing more on zero-shot conversational search approaches. Nevertheless,
existing zero-shot methods face three primary limitations: they are not
universally applicable to all retrievers, their effectiveness lacks sufficient
explainability, and they struggle to resolve common conversational ambiguities
caused by omission. To address these limitations, we introduce a novel
Zero-shot Query Reformulation (ZeQR) framework that reformulates queries based
on previous dialogue contexts without requiring supervision from conversational
search data. Specifically, our framework utilizes language models designed for
machine reading comprehension tasks to explicitly resolve two common
ambiguities: coreference and omission, in raw queries. In comparison to
existing zero-shot methods, our approach is universally applicable to any
retriever without additional adaptation or indexing. It also provides greater
explainability and effectively enhances query intent understanding because
ambiguities are explicitly and proactively resolved. Through extensive
experiments on four TREC conversational datasets, we demonstrate the
effectiveness of our method, which consistently outperforms state-of-the-art
baselines.Comment: Accepted by the 9th ACM SIGIR International Conference on the Theory
of Information Retrieva
5IDER: Unified Query Rewriting for Steering, Intent Carryover, Disfluencies, Entity Carryover and Repair
Providing voice assistants the ability to navigate multi-turn conversations
is a challenging problem. Handling multi-turn interactions requires the system
to understand various conversational use-cases, such as steering, intent
carryover, disfluencies, entity carryover, and repair. The complexity of this
problem is compounded by the fact that these use-cases mix with each other,
often appearing simultaneously in natural language. This work proposes a
non-autoregressive query rewriting architecture that can handle not only the
five aforementioned tasks, but also complex compositions of these use-cases. We
show that our proposed model has competitive single task performance compared
to the baseline approach, and even outperforms a fine-tuned T5 model in
use-case compositions, despite being 15 times smaller in parameters and 25
times faster in latency.Comment: Interspeech 202
Improving Conversational Passage Re-ranking with View Ensemble
This paper presents ConvRerank, a conversational passage re-ranker that
employs a newly developed pseudo-labeling approach. Our proposed view-ensemble
method enhances the quality of pseudo-labeled data, thus improving the
fine-tuning of ConvRerank. Our experimental evaluation on benchmark datasets
shows that combining ConvRerank with a conversational dense retriever in a
cascaded manner achieves a good balance between effectiveness and efficiency.
Compared to baseline methods, our cascaded pipeline demonstrates lower latency
and higher top-ranking effectiveness. Furthermore, the in-depth analysis
confirms the potential of our approach to improving the effectiveness of
conversational search.Comment: SIGIR 202
Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting
Query rewriting plays a vital role in enhancing conversational search by
transforming context-dependent user queries into standalone forms. Existing
approaches primarily leverage human-rewritten queries as labels to train query
rewriting models. However, human rewrites may lack sufficient information for
optimal retrieval performance. To overcome this limitation, we propose
utilizing large language models (LLMs) as query rewriters, enabling the
generation of informative query rewrites through well-designed instructions. We
define four essential properties for well-formed rewrites and incorporate all
of them into the instruction. In addition, we introduce the role of rewrite
editors for LLMs when initial query rewrites are available, forming a
"rewrite-then-edit" process. Furthermore, we propose distilling the rewriting
capabilities of LLMs into smaller models to reduce rewriting latency. Our
experimental evaluation on the QReCC dataset demonstrates that informative
query rewrites can yield substantially improved retrieval performance compared
to human rewrites, especially with sparse retrievers.Comment: 22 pages, accepted to EMNLP Findings 202
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