Thailand

Humans have clear cross-modal preferences when matching certain novel words to visual shapes. Evidence suggests that these preferences play a prominent role in our linguistic processing, language learning, and the origins of signal-meaning mappings. With the rise of multimodal models in AI, such as vision-and-language (VLM) models, it becomes increasingly important to uncover the kinds of visio-linguistic associations these models encode and whether they align with human representations. Informed by experiments with humans, we probe and compare four VLMs for a well-known human cross-modal preference, the bouba-kiki effect. We do not find conclusive evidence for this effect but suggest that results may depend on features of the models, such as architecture design, model size, and training details. Our findings inform discussions on the origins of the bouba-kiki effect in human cognition and future developments of VLMs that align well with human cross-modal associations.

ACL 2024

What does Kiki look like? Cross-modal associations between speech sounds and visual shapes in vision-and-language models

linguistic patterns

cross-modal associations

vision-and-language models

representations

workshop paper

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. Our Virtual Poster Sessions will take place online Thursday, August 22, 2024.

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

This study investigates the performance of SigLIP, a state-of-the-art Vision-Language Model (VLM), in predicting labels for images depicting 1,278 concepts. Our analysis across 300 images per concept shows that the model frequently predicts the exact user-tagged labels, but similarly, it often predicts labels that are semantically related to the exact labels in various ways: synonyms, hypernyms, co-hyponyms, and associated words, particularly for abstract concepts. We then zoom into the diversity of the user tags of images and word associations for abstract versus concrete concepts. Surprisingly, not only abstract but also concrete concepts exhibit significant variability, thus challenging the traditional view that representations of concrete concepts are less diverse.

Evaluating Semantic Relations in Predicting Textual Labels for Images of Abstract and Concrete Concepts

Diachronic corpus analyses reveal that syntactic usage patterns change over time. Are these changes reflected in differences in language processing across the human lifespan? We use the attachment of with-prepositional phrases (PPs) as a case study for investigating this question: a with-PP can attach to a verb, describing an instrument with which to perform the action (e.g., Slice the cake [with a knife]), or to a direct object (DO), modifying the noun (e.g., Slice the cake [with the pink frosting]). The relative frequencies of the instrument and modifier constructions differ depending on the verb in the sentence --- the `verb bias''. Using two diachronic corpora, Syntgram and CCOHA, we analyzed the co-occurrence statistics of 27 verbs and instrument vs. modifier with-PPs. Between the 1940s and the 2000s, some verbs were more instrument-biased (i.e., more likely to co-occur with with-PPs that attach to the verb than the DO) than others and co-occurrence patterns were more similar for temporally close decades, suggesting subtle diachronic changes in usage patterns. We collected sentence interpretation data probing with-PP attachment preferences in participants ranging in age from 25 to 75. Interpretations of globally ambiguous sentences (e.g., Pet the rabbit with the towel) differed depending on the verb (i.e., some verbs elicit more instrument than modifier interpretations of the PP than others and vice versa) and on the age of the participant. In particular, verbs which became less instrument-biased over time elicited more instrument interpretations among older adults than young adults, suggesting that variation in language comprehension can be in part predicted from the corpus statistics of the time periods that an individual experienced.

Diachronic change in verb usage statistics predicts differences in sentence processing across the lifespan

This paper investigates the adverbial discourse particle actually. We compare LLM and human performance on cloze tests involving actually on examples sourced from the Providence Corpus of speech around children. We explore the impact of utterance context on cloze test performance. We find that context is always helpful, though the extent to which additional context is helpful, and what relative placement of context (i.e. before or after the masked word) is most helpful differs for individual models and humans. The best-performing LLM, GPT-4, narrowly outperforms humans. In an additional experiment, we explore cloze performance on synthetic LLM-generated examples, and find that several models vastly outperform humans.

How Useful is Context, Actually? Comparing LLMs and Humans on Discourse Marker Prediction

Rule-based language processing systems have been overshadowed by neural systems in terms of utility, but it remains unclear whether neural NLP systems, in practice, learn the grammar rules that humans use. This work aims to shed light on the issue by evaluating state-of-the-art LLMs in a task of morphological analysis of complex Finnish noun forms. We generate the forms using an FST tool, and they are unlikely to have occurred in the training sets of the LLMs, therefore requiring morphological generalisation capacity. We find that GPT-4-turbo has some difficulties in the task while GPT-3.5-turbo struggles and smaller models Llama2-70B and Poro-34B fail nearly completely.

Rules in nets? LLMs’ morphological analyses of rare and complex FST-generated Finnish word forms

Application of LLM to database queries on natural language sentences has demonstrated impressive results in both single and multi-hop scenarios.
In the existing methodologies, the requirement to re-encode query vectors at each stage for processing multi-hop queries presents a significant bottleneck to the inference speed.
This paper proposes VKGFR (Virtual Knowledge Graph based Fact Retriever) that leverages large language models to extract representations corresponding to a sentence's knowledge graph, significantly enhancing inference speed for multi-hop reasoning without performance loss.
Given that both the queries and natural language database sentences can be structured as a knowledge graph, we suggest extracting a Virtual Knowledge Graph (VKG) representation from sentences with LLM.
Over the pre-constructed VKG, our VKGFR conducts retrieval with a tiny model structure, showing performance improvements with higher computational efficiency. 
We evaluate VKGFR on the WikiNLDB and MetaQA dataset, designed for multi-hop database reasoning over text. 
The results indicate 13x faster inference speed on the WikiNLDB dataset without performance loss.

Multi-hop Database Reasoning with Virtual Knowledge Graph

In this work, we tested the Triplet Extraction (TE) capabilities of a variety of Large Language Models (LLMs) of different sizes in the Zero- and Few-Shots settings. In detail, we proposed a pipeline that dynamically gathers contextual information from a Knowledge Base (KB), both in the form of context triplets and of (sentence, triplets) pairs as examples, and provides it to the LLM through a prompt. The additional context allowed the LLMs to be competitive with all the older fully trained baselines based on the Bidirectional Long Short-Term Memory (BiLSTM) Network architecture. 
We further conducted a detailed analysis of the quality of the gathered KB context, finding it to be strongly correlated with the final TE performance of the model. In contrast, the size of the model appeared to only logarithmically improve the TE capabilities of the LLMs. We release the code on GitHub for reproducibility.

Zero- and Few-Shots Knowledge Graph Triplet Extraction with Large Language Models

Recent LLMs show an impressive accuracy on one of the hallmark tasks of language understanding, namely Question Answering (QA). However, it is not clear if the correct answers provided by LLMs are actually grounded on the correct knowledge related to the question. In this paper, we use multi-hop QA datasets to evaluate the accuracy of the knowledge LLMs use to answer questions, and show that as much as 31% of the correct answers by the LLMs are in fact spurious, i.e., the knowledge LLMs used to ground the answer is wrong while the answer is correct. We present an analysis of these spurious correct answers by GPT-4 using three datasets in two languages, while suggesting future pathways to correct the grounding information using existing external knowledge bases.

Analysis of LLM’s “Spurious” Correct Answers Using Evidence Information of Multi-hop QA Datasets

Generative AI and Large Language Models are increasingly used in business contexts. One application involves natural language conversations contextualized by company data, which can be accomplished by Enterprise Knowledge Graphs, standardized representations of data. This paper outlines an architecture for implementation of an Enterprise Knowledge Graph using open-source Wikibase software. Additionally, it is presented a Knowledge Graph Q&A System powered by Generative AI.

Application of Generative AI as an Enterprise Wikibase Knowledge Graph Q&A System

In recent years, the use of synthetic data, either as a complement or a substitute for original data, has emerged as a solution to challenges such as data scarcity and security risks. This paper is an initial attempt to automatically generate such data for Information Extraction tasks. We accomplished this by developing a novel synthetic data generation framework called KGAST, which leverages Knowledge Graphs and Large Language Models. In our preliminary study, we conducted simple experiments to generate synthetic versions of two datasets—a French security defense dataset and an English general domain dataset, after which we evaluated them both intrinsically and extrinsically. The results indicated that synthetic data can effectively complement original data, improving the performance of models on classes with limited training samples. This highlights KGAST's potential as a tool for generating synthetic data for Information Extraction tasks.

KGAST: From Knowledge Graphs to Annotated Synthetic Texts

Knowledge Graphs (KGs) serving as semantic networks, prove highly effective in managing complex interconnected data in different domains, by offering a unified, contextualized, and structured representation with flexibility that allows for easy adaptation to evolving knowledge. Processing complex Human Resources (HR) data, KGs can help in different HR functions like recruitment, job matching, identifying learning gaps, and enhancing employee retention. Despite their potential, limited efforts have been made to implement practical HR knowledge graphs. This study addresses this gap by presenting a framework for effectively developing HR knowledge graphs from documents using Large Language Models. The resulting KG can be used for a variety of downstream tasks, including job matching, identifying employee skill gaps, and many more. In this work, we showcase instances where HR KGs prove instrumental in precise job matching, yielding advantages for both employers and employees. Empirical evidence from experiments with information propagation in KGs and Graph Neural Nets, along with case studies underscores the effectiveness of KGs in tasks such as job and employee recommendations and job area classification. Code and data are available at : https://github.com/azminewasi/HRGraph

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What does Kiki look like? Cross-modal associations between speech sounds and visual shapes in vision-and-language models

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Evaluating Semantic Relations in Predicting Textual Labels for Images of Abstract and Concrete Concepts

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