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Found 21 papers, 12 papers with code
Who Evaluates the Evaluations? Objectively Scoring Text-to-Image Prompt Coherence Metrics with T2IScoreScore (TS2)
With advances in the quality of text-to-image (T2I) models has come interest in benchmarking their prompt faithfulness-the semantic coherence of generated images to the prompts they were conditioned on.
Lost in Translation? Translation Errors and Challenges for Fair Assessment of Text-to-Image Models on Multilingual Concepts
Benchmarks of the multilingual capabilities of text-to-image (T2I) models compare generated images prompted in a test language to an expected image distribution over a concept set.
Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies
Large language models (LLMs) have demonstrated remarkable performance across a wide array of NLP tasks.
Multilingual Conceptual Coverage in Text-to-Image Models
We propose "Conceptual Coverage Across Languages" (CoCo-CroLa), a technique for benchmarking the degree to which any generative text-to-image system provides multilingual parity to its training language in terms of tangible nouns.
Let's Think Frame by Frame with VIP: A Video Infilling and Prediction Dataset for Evaluating Video Chain-of-Thought
Despite exciting recent results showing vision-language systems' capacity to reason about images using natural language, their capacity for video reasoning remains under-explored.
Data Augmentation for Diverse Voice Conversion in Noisy Environments
Voice conversion (VC) models have demonstrated impressive few-shot conversion quality on the clean, native speech populations they're trained on.
Visual Chain of Thought: Bridging Logical Gaps with Multimodal Infillings
Recent advances in large language models elicit reasoning in a chain-of-thought that allows models to decompose problems in a human-like fashion.
Users are the North Star for AI Transparency
We conduct a broad literature survey, identifying many clusters of similar conceptions of transparency, tying each back to our north star with analysis of how it furthers or hinders our ideal AI transparency goals.
Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning
This study aims to examine the in-context learning phenomenon through a Bayesian lens, viewing real-world LLMs as latent variable models.
CausalDialogue: Modeling Utterance-level Causality in Conversations
Despite their widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans.
WikiWhy: Answering and Explaining Cause-and-Effect Questions
As large language models (LLMs) grow larger and more sophisticated, assessing their "reasoning" capabilities in natural language grows more challenging.
Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis
Is it possible to build a general and automatic natural language generation (NLG) evaluation metric?
Causal Balancing for Domain Generalization
While machine learning models rapidly advance the state-of-the-art on various real-world tasks, out-of-domain (OOD) generalization remains a challenging problem given the vulnerability of these models to spurious correlations.
PECO: Examining Single Sentence Label Leakage in Natural Language Inference Datasets through Progressive Evaluation of Cluster Outliers
Building natural language inference (NLI) benchmarks that are both challenging for modern techniques, and free from shortcut biases is difficult.
Self-Supervised Knowledge Assimilation for Expert-Layman Text Style Transfer
This is a particularly notable issue in the medical domain, where layman are often confused by medical text online.
End-to-End Spoken Language Understanding for Generalized Voice Assistants
End-to-end (E2E) spoken language understanding (SLU) systems predict utterance semantics directly from speech using a single model.
Ranked #10 on
Spoken Language Understanding
on Fluent Speech Commands
(using extra training data)
Counterfactual Maximum Likelihood Estimation for Training Deep Networks
Although deep learning models have driven state-of-the-art performance on a wide array of tasks, they are prone to spurious correlations that should not be learned as predictive clues.
Investigating Memorization of Conspiracy Theories in Text Generation
In this work, we investigate the capability of language models to generate conspiracy theory text.
Modeling Disclosive Transparency in NLP Application Descriptions
Broader disclosive transparency$-$truth and clarity in communication regarding the function of AI systems$-$is widely considered desirable.
Semantic Complexity in End-to-End Spoken Language Understanding
We perform experiments where we vary the semantic complexity of a large, proprietary dataset and show that STI model performance correlates with our semantic complexity measures, such that performance increases as complexity values decrease.
Robust Estimation of Hypernasality in Dysarthria with Acoustic Model Likelihood Features
To demonstrate that the features derived from these acoustic models are specific to hypernasal speech, we evaluate them across different dysarthria corpora.
