Search Results for author:
Found 15 papers, 6 papers with code
Generative Query Reformulation Using Ensemble Prompting, Document Fusion, and Relevance Feedback
Query Reformulation (QR) is a set of techniques used to transform a user's original search query to a text that better aligns with the user's intent and improves their search experience.
DUQGen: Effective Unsupervised Domain Adaptation of Neural Rankers by Diversifying Synthetic Query Generation
State-of-the-art neural rankers pre-trained on large task-specific training data such as MS-MARCO, have been shown to exhibit strong performance on various ranking tasks without domain adaptation, also called zero-shot.
QueryExplorer: An Interactive Query Generation Assistant for Search and Exploration
To enable exploration and to support Human-In-The-Loop experiments we propose QueryExplorer -- an interactive query generation, reformulation, and retrieval interface with support for HuggingFace generation models and PyTerrier's retrieval pipelines and datasets, and extensive logging of human feedback.
KAUCUS: Knowledge Augmented User Simulators for Training Language Model Assistants
Through two GPT-J based simulators viz., a Retrieval Augmented Simulator and a Summary Controlled Simulator we generate diverse simulator-assistant interactions.
An Interactive Query Generation Assistant using LLM-based Prompt Modification and User Feedback
While search is the predominant method of accessing information, formulating effective queries remains a challenging task, especially for situations where the users are not familiar with a domain, or searching for documents in other languages, or looking for complex information such as events, which are not easily expressible as queries.
NusaCrowd: Open Source Initiative for Indonesian NLP Resources
We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
A Bird's-Eye Tutorial of Graph Attention Architectures
Graph Neural Networks (GNNs) have shown tremendous strides in performance for graph-structured problems especially in the domains of natural language processing, computer vision and recommender systems.
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
CANDLE: Decomposing Conditional and Conjunctive Queries for Task-Oriented Dialogue Systems
Domain-specific dialogue systems generally determine user intents by relying on sentence level classifiers that mainly focus on single action sentences.
Automatic Construction of Evaluation Suites for Natural Language Generation Datasets
By applying this framework to the GEM generation benchmark, we propose an evaluation suite made of 80 challenge sets, demonstrate the kinds of analyses that it enables and shed light onto the limits of current generation models.
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics.
Ranked #1 on
Extreme Summarization
on GEM-XSum
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+5
Resolving Intent Ambiguities by Retrieving Discriminative Clarifying Questions
However, user queries appearing in natural language can be easily ambiguous and hence such a direct mapping might not be straightforward harming intent detection and eventually the overall performance of a dialogue system.
Benchmarking BioRelEx for Entity Tagging and Relation Extraction
Extracting relationships and interactions between different biological entities is still an extremely challenging problem but has not received much attention as much as extraction in other generic domains.
Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation
Question Generation (QG) is fundamentally a simple syntactic transformation; however, many aspects of semantics influence what questions are good to form.
