Conversational Search
59 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Conversational Search
ICConv
Most implemented papers
V3CTRON | Data Retrieval & Access System For Flexible Semantic Search & Retrieval Of Proprietary Document Collections Using Natural Language Queries.
serp-ai/V3CTRON-vector-database-embedding-neural-search-retrieval-chatgpt-plugin
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Social Science Research Network (SSRN) 2023
V3CTRON is an open source vector database that allows users to upload text based documents & document collections, which are automatically embedded for super-accurate semantic search & retrieval using natural language queries.
Attentive History Selection for Conversational Question Answering
prdwb/attentive_history_selection
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First, we propose a positional history answer embedding method to encode conversation history with position information using BERT in a natural way.
Introducing MANtIS: a novel Multi-Domain Information Seeking Dialogues Dataset
Conversational search is an approach to information retrieval (IR), where users engage in a dialogue with an agent in order to satisfy their information needs.
Large Language Models Know Your Contextual Search Intent: A Prompting Framework for Conversational Search
kyriemao/llm4cs
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Precisely understanding users' contextual search intent has been an important challenge for conversational search.
Detecting Generated Native Ads in Conversational Search
webis-de/ads-in-generative-ir
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In this paper, we thus take a first step to investigate whether LLMs can also be used as a countermeasure, i. e., to block generated native ads.
Ranking Manipulation for Conversational Search Engines
These conversational search engines operate by loading retrieved website text into the LLM context for summarization and interpretation.
Attentive Memory Networks: Efficient Machine Reading for Conversational Search
TomKenter/attentive-memory-networks-code
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We argue that the process of building a representation of the conversation can be framed as a machine reading task, where an automated system is presented with a number of statements about which it should answer questions.
User Intent Prediction in Information-seeking Conversations
Due to the limited communication bandwidth in conversational search, it is important for conversational assistants to accurately detect and predict user intent in information-seeking conversations.
BERT with History Answer Embedding for Conversational Question Answering
prdwb/bert_hae
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One of the major challenges to multi-turn conversational search is to model the conversation history to answer the current question.
TREC CAsT 2019: The Conversational Assistance Track Overview
A common theme through the runs is the use of BERT-based neural reranking methods.
