# RALI@iKAT 2026: Retrieval-aware User Profile Denoising via Personalized Conversational Dense Retrieval

arXiv: https://arxiv.org/abs/2610.17673 · HTML: https://mlsanity.com/paper/2610.17673/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 38 of its 71 references that are in the map.
- First posted on arXiv 13 Oct 2026.

## Builds on (most cited first, 30 of 38)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models](https://mlsanity.com/paper/2506.05176/) (2025)
- [Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers](https://mlsanity.com/paper/2103.15679/) (2021)
- [Fine-Tuning LLaMA for Multi-Stage Text Retrieval](https://mlsanity.com/paper/2310.08319/) (2023)
- [Embedding-based Retrieval in Facebook Search](https://mlsanity.com/paper/2006.11632/) (2020)
- [TopiOCQA: Open-domain Conversational Question Answering with Topic Switching](https://mlsanity.com/paper/2110.00768/) (2021)
- [Large Language Models Know Your Contextual Search Intent: A Prompting Framework for Conversational Search](https://mlsanity.com/paper/2303.06573/) (2023)
- [Few-Shot Conversational Dense Retrieval](https://mlsanity.com/paper/2105.04166/) (2021)
- [SPLADE-v3: New baselines for SPLADE](https://mlsanity.com/paper/2403.06789/) (2024)
- [Embedding-based Product Retrieval in Taobao Search](https://mlsanity.com/paper/2106.09297/) (2021)
- [Neural Approaches to Conversational Information Retrieval](https://mlsanity.com/paper/2201.05176/) (2022)
- [Promptriever: Instruction-Trained Retrievers Can Be Prompted Like Language Models](https://mlsanity.com/paper/2409.11136/) (2024)
- [Contextualized Query Embeddings for Conversational Search](https://mlsanity.com/paper/2104.08707/) (2021)
- [INSTRUCTIR: A Benchmark for Instruction Following of Information Retrieval Models](https://mlsanity.com/paper/2402.14334/) (2024)
- [Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning](https://mlsanity.com/paper/2006.02282/) (2020)
- [Exploiting Simulated User Feedback for Conversational Search: Ranking, Rewriting, and Beyond](https://mlsanity.com/paper/2304.13874/) (2023)
- [Saving Dense Retriever from Shortcut Dependency in Conversational Search](https://mlsanity.com/paper/2202.07280/) (2022)
- [ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval](https://mlsanity.com/paper/2404.13556/) (2024)
- [Generalizing Conversational Dense Retrieval via LLM-Cognition Data Augmentation](https://mlsanity.com/paper/2402.07092/) (2024)
- [TREC iKAT 2023: A Test Collection for Evaluating Conversational and Interactive Knowledge Assistants](https://mlsanity.com/paper/2405.02637/) (2024)
- [A Transformer-based Embedding Model for Personalized Product Search](https://mlsanity.com/paper/2005.08936/) (2020)
- [A Zero Attention Model for Personalized Product Search](https://mlsanity.com/paper/1908.11322/) (2019)
- [Analysing Mixed Initiatives and Search Strategies during Conversational Search](https://mlsanity.com/paper/2109.05955/) (2021)
- [UniConv: Unifying Retrieval and Response Generation for Large Language Models in Conversations](https://mlsanity.com/paper/2507.07030/) (2025)
- [How to Leverage Personal Textual Knowledge for Personalized Conversational Information Retrieval](https://mlsanity.com/paper/2407.16192/) (2024)
- [Aligning Query Representation with Rewritten Query and Relevance Judgments in Conversational Search](https://mlsanity.com/paper/2407.20189/) (2024)
- [DiSCo: LLM Knowledge Distillation for Efficient Sparse Retrieval in Conversational Search](https://mlsanity.com/paper/2410.14609/) (2024)
- [A Personalized Dense Retrieval Framework for Unified Information Access](https://mlsanity.com/paper/2304.13654/) (2023)
- [Learning Contextual Retrieval for Robust Conversational Search](https://mlsanity.com/paper/2509.19700/) (2025)

## Abstract (arXiv)

Modern conversational information access systems continuously extract user information from conversations to build user profiles for personalized retrieval. As user profiles accumulate over time, they inevitably become noisy, making effective user profile denoising a fundamental challenge. Existing personalized conversational query reformulation methods rely on the black-box reasoning capability of LLMs to expand the original query with relevant user profile phrases, producing a verbose query for retrieval. Such rewrite-then-retrieve pipelines reduce user profile denoising to the explicit selection of profile phrases in the sparse token space, separating personalization from downstream relevance modeling. Consequently, they often fail to identify user information that is truly beneficial for retrieval. To enable retrieval-aware user profile denoising, we propose Personalized Conversational Dense Retrieval (PCDR), which learns to jointly project the conversation history and user profile into a latent embedding space dedicated to retrieval. By inheriting the context denoising capability from conversational dense retrievers, PCDR requires only a handful of personalized relevance annotations for training. We evaluate PCDR on the iKAT 2026 tasks, where it serves as the core retrieval component of a conversational AI agent assisting users with complex information needs.