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Found 30 papers, 14 papers with code
What Makes Multimodal In-Context Learning Work?
Large Language Models have demonstrated remarkable performance across various tasks, exhibiting the capacity to swiftly acquire new skills, such as through In-Context Learning (ICL) with minimal demonstration examples.
PAQA: Toward ProActive Open-Retrieval Question Answering
Conversational systems have made significant progress in generating natural language responses.
LOCOST: State-Space Models for Long Document Abstractive Summarization
State-space models are a low-complexity alternative to transformers for encoding long sequences and capturing long-term dependencies.
Simple Domain Adaptation for Sparse Retrievers
In Information Retrieval, and more generally in Natural Language Processing, adapting models to specific domains is conducted through fine-tuning.
Navigating Uncertainty: Optimizing API Dependency for Hallucination Reduction in Closed-Book Question Answering
Our model directly provides answers for $78. 2\%$ of the known queries and opts to search for $77. 2\%$ of the unknown ones.
Augmenting Ad-Hoc IR Dataset for Interactive Conversational Search
The only two datasets known to us that contain both document relevance judgments and the associated clarification interactions are Qulac and ClariQ.
CIRCLE: Multi-Turn Query Clarifications with Reinforcement Learning
In this paper, we introduce CIRCLE, a generative model for multi-turn query Clarifications wIth ReinforCement LEarning that leverages multi-turn interactions through a user simulation framework.
Improving generalization in large language models by learning prefix subspaces
We show in this paper that "Parameter Efficient Fine-Tuning" (PEFT) methods, however, are perfectly compatible with this original approach, and propose to learn entire simplex of continuous prefixes.
Dynamic Named Entity Recognition
Named Entity Recognition (NER) is a challenging and widely studied task that involves detecting and typing entities in text.
CoSPLADE: Contextualizing SPLADE for Conversational Information Retrieval
Conversational search is a difficult task as it aims at retrieving documents based not only on the current user query but also on the full conversation history.
Building a Subspace of Policies for Scalable Continual Learning
We introduce Continual Subspace of Policies (CSP), a new approach that incrementally builds a subspace of policies for training a reinforcement learning agent on a sequence of tasks.
Interactive Query Clarification and Refinement via User Simulation
When users initiate search sessions, their queries are often unclear or might lack of context; this resulting in inefficient document ranking.
Continual Learning of Long Topic Sequences in Neural Information Retrieval
In information retrieval (IR) systems, trends and users' interests may change over time, altering either the distribution of requests or contents to be recommended.
State of the Art of User Simulation approaches for conversational information retrieval
Conversational Information Retrieval (CIR) is an emerging field of Information Retrieval (IR) at the intersection of interactive IR and dialogue systems for open domain information needs.
Does Structure Matter? Leveraging Data-to-Text Generation for Answering Complex Information Needs
In this work, our aim is to provide a structured answer in natural language to a complex information need.
Learning a subspace of policies for online adaptation in Reinforcement Learning
There is a need to develop RL methods that generalize well to variations of the training conditions.
Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluation
QuestEval is a reference-less metric used in text-to-text tasks, that compares the generated summaries directly to the source text, by automatically asking and answering questions.
Controlling Hallucinations at Word Level in Data-to-Text Generation
Specifically, we propose a Multi-Branch Decoder which is able to leverage word-level labels to learn the relevant parts of each training instance.
Ranked #3 on
Table-to-Text Generation
on WikiBio
Studying Catastrophic Forgetting in Neural Ranking Models
Several deep neural ranking models have been proposed in the recent IR literature.
PARENTing via Model-Agnostic Reinforcement Learning to Correct Pathological Behaviors in Data-to-Text Generation
Evaluations on the widely used WikiBIO and WebNLG benchmarks demonstrate the effectiveness of this framework compared to state-of-the-art models.
Incorporating Visual Semantics into Sentence Representations within a Grounded Space
To overcome this limitation, we propose to transfer visual information to textual representations by learning an intermediate representation space: the grounded space.
Conversational Search for Learning Technologies
Conversational search is based on a user-system cooperation with the objective to solve an information-seeking task.
A Hierarchical Model for Data-to-Text Generation
This however loses most of the structure contained in the data.
Context-Aware Zero-Shot Learning for Object Recognition
Zero-Shot Learning (ZSL) aims at classifying unlabeled objects by leveraging auxiliary knowledge, such as semantic representations.
A Reinforcement Learning-driven Translation Model for Search-Oriented Conversational Systems
Search-oriented conversational systems rely on information needs expressed in natural language (NL).
Images & Recipes: Retrieval in the cooking context
Recent advances in the machine learning community allowed different use cases to emerge, as its association to domains like cooking which created the computational cuisine.
Cross-Modal Retrieval in the Cooking Context: Learning Semantic Text-Image Embeddings
Designing powerful tools that support cooking activities has rapidly gained popularity due to the massive amounts of available data, as well as recent advances in machine learning that are capable of analyzing them.
Ranked #9 on
Cross-Modal Retrieval
on Recipe1M
Learning Multi-Modal Word Representation Grounded in Visual Context
Representing the semantics of words is a long-standing problem for the natural language processing community.
DSRIM: A Deep Neural Information Retrieval Model Enhanced by a Knowledge Resource Driven Representation of Documents
The state-of-the-art solutions to the vocabulary mismatch in information retrieval (IR) mainly aim at leveraging either the relational semantics provided by external resources or the distributional semantics, recently investigated by deep neural approaches.
Toward a Deep Neural Approach for Knowledge-Based IR
With this in mind, we argue that embedding KBs within deep neural architectures supporting documentquery matching would give rise to fine-grained latent representations of both words and their semantic relations.
