Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Large Language Models (LLMs) have shown human-like reasoning abilities but still struggle with complex logical problems. This paper introduces a novel framework, Logic-LM, which integrates LLMs with symbolic solvers to improve logical problem-solving. Our method first utilizes LLMs to translate a natural language problem into a symbolic formulation. Afterward, a deterministic symbolic solver performs inference on the formulated problem. We also introduce a self-refinement module, which utilizes the symbolic solver's error messages to revise symbolic formalizations. We demonstrate Logic-LM's effectiveness on five logical reasoning datasets: ProofWriter, PrOntoQA, FOLIO, LogicalDeduction, and AR-LSAT. On average, Logic-LM achieves a significant performance boost of 39.2% over using LLM alone with standard prompting and 18.4% over LLM with chain-of-thought prompting. Our findings suggest that Logic-LM, by combining LLMs with symbolic logic, offers a promising avenue for faithful logical reasoning. Code and data are publicly available at https://github.com/teacherpeterpan/Logic-LLM.Comment: EMNLP 2023 (Findings, long paper

Data-Efficiency with a Single GPU: An Exploration of Transfer Methods for Small Language Models

Multi-task learning (MTL), instruction tuning, and prompting have recently been shown to improve the generalizability of large language models to new tasks. However, the benefits of such methods are less well-documented in smaller language models, with some studies finding contradictory results. In this work, we explore and isolate the effects of (i) model size, (ii) general purpose MTL, (iii) in-domain MTL, (iv) instruction tuning, and (v) few-shot fine-tuning for models with fewer than 500 million parameters. Our experiments in the zero-shot setting demonstrate that models gain 31% relative improvement, on average, from general purpose MTL, with an additional 37.6% relative gain from in-domain MTL. Contradictory to prior works on large models, we find that instruction tuning provides a modest 2% performance improvement for small models

Emotion Recognition in Conversation using Probabilistic Soft Logic

Creating agents that can both appropriately respond to conversations and understand complex human linguistic tendencies and social cues has been a long standing challenge in the NLP community. A recent pillar of research revolves around emotion recognition in conversation (ERC); a sub-field of emotion recognition that focuses on conversations or dialogues that contain two or more utterances. In this work, we explore an approach to ERC that exploits the use of neural embeddings along with complex structures in dialogues. We implement our approach in a framework called Probabilistic Soft Logic (PSL), a declarative templating language that uses first-order like logical rules, that when combined with data, define a particular class of graphical model. Additionally, PSL provides functionality for the incorporation of results from neural models into PSL models. This allows our model to take advantage of advanced neural methods, such as sentence embeddings, and logical reasoning over the structure of a dialogue. We compare our method with state-of-the-art purely neural ERC systems, and see almost a 20% improvement. With these results, we provide an extensive qualitative and quantitative analysis over the DailyDialog conversation dataset

CausalDialogue: Modeling Utterance-level Causality in Conversations

Despite their widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans. In this research, we examine user utterances as causes and generated responses as effects, recognizing that changes in a cause should produce a different effect. To further explore this concept, we have compiled and expanded upon a new dataset called CausalDialogue through crowd-sourcing. This dataset includes multiple cause-effect pairs within a directed acyclic graph (DAG) structure. Our analysis reveals that traditional loss functions struggle to effectively incorporate the DAG structure, leading us to propose a causality-enhanced method called Exponential Maximum Average Treatment Effect (ExMATE) to enhance the impact of causality at the utterance level in training neural conversation models. To evaluate the needs of considering causality in dialogue generation, we built a comprehensive benchmark on CausalDialogue dataset using different models, inference, and training methods. Through experiments, we find that a causality-inspired loss like ExMATE can improve the diversity and agility of conventional loss function and there is still room for improvement to reach human-level quality on this new dataset.Comment: Accepted to ACL-Findings 202

Characteristics of indoor/outdoor particulate pollution in urban and rural residential environment of Pakistan

Particulate pollution has emerged as a serious environmental health concern in Pakistan. The use of biomass fuels in traditional stoves produces high levels of indoor air pollutants. In Pakistan, 94% of rural and 58% of urban households depend on biomass fuel. This study investigates variations in indoor/outdoor concentrations of particulate matter during various activities for three different micro-environments in Pakistan. At a rural site, the average indoor/outdoor ratios for PM10, PM2.5, and PM 1, in kitchens using biomass fuels were 3.80, 4.36, and 4.11, respectively. A large variation was recorded in the mass concentration of particulate matter during cooking with concentrations in the range 4000-8555 μg/m3. In a living room at a rural site, the average indoor/outdoor ratios for PM10, PM2.5, and PM1 were 1.74, 2.49, and 3.01, respectively. At the urban site, the average indoor/outdoor ratios for the same size fractions were 1.71, 2.88, and 3.47, respectively. Cooking, cleaning and smoking were identified as principal contributors to the high indoor levels of particulate matter. This study showed considerably high concentrations of particulate matter, particularly in kitchens using biomass fuels, as compared to living areas. Thus women and children face the greatest exposure due to the amount of time they spend in the kitchen. Practical Implications In the developing world, particulate air pollution, both indoor and outdoor, is a substantial health hazard to the public. The very high concentrations of particulate matter in both rural and urban sites, particularly in kitchens using biomass fuels emphasize the severity of this issue in Pakistan. Women and children are extensively at risk due to amount of time spent in kitchens. This state of affairs calls for a large-scale intervention to reduce the exposure to indoor air pollution. © 2009 John Wiley & Sons A/S

Poor concordance between interferon-γ release assays and tuberculin skin tests in diagnosis of latent tuberculosis infection among HIV-infected individuals

<p>Abstract</p> <p>Background</p> <p>A new generation of diagnostic tests, the interferon-γ release assays (IGRAs), have been developed for the detection of latent tuberculosis infection (LTBI). Limited data are available on their use in HIV-infected persons.</p> <p>Methods</p> <p>A cross-sectional study was carried out at 2 HIV clinics in Atlanta to assess the utility of two IGRA tests (T-SPOT.TB [TSPOT] and QuantiFERON-TB Gold in Tube [QFT-3G]) compared to the tuberculin skin test (TST).</p> <p>Results</p> <p>336 HIV-infected persons were enrolled. Median CD4 count was 335 cells/μl and median HIV viral load was 400 copies/ml. Overall, 27 patients (8.0%) had at least 1 positive diagnostic test for LTBI: 7 (2.1%) had a positive TST; 9 (2.7%) a positive QFT-3G; and 14 (4.2%) a positive TSPOT. Agreement between the 3 diagnostic tests was poor: TST and TSPOT, [κ = 0.16, 95% CI (-0.06, 0.39)], TST and QFT-3G [κ = 0.23, 95% CI (-0.05, 0.51)], QFT-3G and TSPOT [κ = 0.06, 95% CI (-0.1, 0.2)]. An indeterminate test result occurred among 6 (1.8%) of QFT-3G and 47 (14%) of TSPOT tests. In multivariate analysis, patients with a CD4 ≤ 200 cells/μl were significantly more likely to have an indeterminate result [OR = 3.6, 95% CI (1.9, 6.8)].</p> <p>Conclusion</p> <p>We found a low prevalence of LTBI and poor concordance between all 3 diagnostic tests. Indeterminate test results were more likely at CD4 counts ≤ 200 cells/μl. Additional studies among HIV-infected populations with a high prevalence of TB are needed to further assess the utility of IGRAs in this patient population.</p

Follow-up study on lead exposure in children living in a smelter community in northern Mexico

<p>Abstract</p> <p>Background</p> <p>To study the changes of children lead exposure in the city of Torreon during the last five years, after environmental and public health interventions, using the timeline of lead in blood concentration as the biomarker of exposure and its relation to lead in soil concentrations.</p> <p>Methods</p> <p>This follow-up study started in 2001 and consisted of 232 children living in nine neighborhoods in Torreon. Children were tested at 0, 6, 12 and 60 months. Lead in blood concentrations, Hemoglobin, Zinc-Protoporphyrin, anthropometric measures and socioeconomic status questionnaire was supplied to the parents.</p> <p>Results</p> <p>Median and range of lead in blood concentrations obtained at 0, 6, 12, 60 months were: 10.12 μg/dl (1.9 - 43.8), 8.75 μg/dl (1.85 - 41.45), 8.4 μg/dl (1.7 - 35.8) and 4.4 μg/dl (1.3 - 30.3), respectively. The decrease of lead in blood levels was significantly related to ages 0, 6, 12 and 60 months of the follow-up study. The timeline of B-Pb was associated with the timeline of lead in soil concentrations.</p> <p>Conclusions</p> <p>B-Pb levels have significantly decreased in the group of children studied. This could be explained by a) environmental interventions by authorities and the smelter companies, b) normal changes in hygienic habits as children age and c) lead redistribution from blood to hard tissues.</p

An influenza A (H3N2) virus outbreak in the Kingdom of Cambodia during the COVID-19 pandemic of 2020.

BACKGROUND: Global influenza virus circulation decreased during the COVID-19 pandemic, possibly due to widespread community mitigation measures. Cambodia eased some COVID-19 mitigation measures in June and July 2020. On 20 August a cluster of respiratory illnesses occurred among residents of a pagoda, including people who tested positive for influenza A but none who were positive for SARS-CoV-2. METHODS: A response team was deployed on 25 August 2020. People with influenza-like illness (ILI) were asked questions regarding demographics, illness, personal prevention measures, and residential arrangements. Respiratory swabs were tested for influenza and SARS-Cov-2 by real-time reverse transcription PCR, and viruses were sequenced. Sentinel surveillance data were analyzed to assess recent trends in influenza circulation in the community. RESULTS: Influenza A (H3N2) viruses were identified during sentinel surveillance in Cambodia in July 2020 prior to the reported pagoda outbreak. Among the 362 pagoda residents, 73 (20.2%) ILI cases were identified and 40 were tested, where 33/40 (82.5%) confirmed positive for influenza A (H3N2). All 40 were negative for SARS-CoV-2. Among the 73 residents with ILI, none were vaccinated against influenza, 47 (64%) clustered in 3/8 sleeping quarters, 20 (27%) reported often wearing a mask, 27 (36%) reported often washing hands, and 11 (15%) reported practicing social distancing. All viruses clustered within clade 3c2.A1 close to strains circulating in Australia in 2020. CONCLUSIONS: Circulation of influenza viruses began in the community following the relaxation of national COVID-19 mitigation measures, and prior to the outbreak in a pagoda with limited social distancing. Continued surveillance and influenza vaccination are required to limit the impact of influenza globally