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Reacted to
averoo's
post with ๐
26 days ago
Post
3773
Hello, researchers! I've tried to made reading HF Daily Papers easier and made a tool that does reviews with LLMs like Claude 3.5, GPT-4o and sometimes FLUX.
๐ Classification by topics
๐ Sorting by publication date and HF addition date
๐ Syncing every 2 hours
๐ป Hosted on GitHub
๐ English, Russian, and Chinese
๐ Top by week/month (in progress)
๐ https://hfday.ru
Let me know what do you think of it.
๐ Classification by topics
๐ Sorting by publication date and HF addition date
๐ Syncing every 2 hours
๐ป Hosted on GitHub
๐ English, Russian, and Chinese
๐ Top by week/month (in progress)
๐ https://hfday.ru
Let me know what do you think of it.
updated
a
collection
about 1 month ago
updated
a
collection
about 2 months ago
Reacted to
singhsidhukuldeep's
post with ๐
about 2 months ago
Post
3986
Researchers have developed a novel approach called Logic-of-Thought (LoT) that significantly enhances the logical reasoning capabilities of large language models (LLMs).
Here are the steps on how Logic-of-Thought (LoT) is implemented:
-- 1. Logic Extraction
1. Use Large Language Models (LLMs) to identify sentences containing conditional reasoning relationships from the input context.
2. Generate a collection of sentences with logical relationships.
3. Use LLMs to extract the set of propositional symbols and logical expressions from the collection.
4. Identify propositions with similar meanings and represent them using identical propositional symbols.
5. Analyze the logical relationships between propositions based on their natural language descriptions.
6. Add negation (ยฌ) for propositions that express opposite meanings.
7. Use implication (โ) to connect propositional symbols when a conditional relationship exists.
-- 2. Logic Extension
1. Apply logical reasoning laws to the collection of logical expressions from the Logic Extraction phase.
2. Use a Python program to implement logical deduction and expand the expressions.
3. Apply logical laws such as Double Negation, Contraposition, and Transitivity to derive new logical expressions.
-- 3. Logic Translation
1. Use LLMs to translate the newly generated logical expressions into natural language descriptions.
2. Combine the natural language descriptions of propositional symbols according to the extended logical expressions.
3. Incorporate the translated logical information as a new part of the original input prompt.
-- 4. Integration with Existing Prompting Methods
1. Combine the LoT-generated logical information with the original prompt.
2. Use this enhanced prompt with existing prompting methods like Chain-of-Thought (CoT), Self-Consistency (SC), or Tree-of-Thoughts (ToT).
3. Feed the augmented prompt to the LLM to generate the final answer.
What do you think about LoT?
Here are the steps on how Logic-of-Thought (LoT) is implemented:
-- 1. Logic Extraction
1. Use Large Language Models (LLMs) to identify sentences containing conditional reasoning relationships from the input context.
2. Generate a collection of sentences with logical relationships.
3. Use LLMs to extract the set of propositional symbols and logical expressions from the collection.
4. Identify propositions with similar meanings and represent them using identical propositional symbols.
5. Analyze the logical relationships between propositions based on their natural language descriptions.
6. Add negation (ยฌ) for propositions that express opposite meanings.
7. Use implication (โ) to connect propositional symbols when a conditional relationship exists.
-- 2. Logic Extension
1. Apply logical reasoning laws to the collection of logical expressions from the Logic Extraction phase.
2. Use a Python program to implement logical deduction and expand the expressions.
3. Apply logical laws such as Double Negation, Contraposition, and Transitivity to derive new logical expressions.
-- 3. Logic Translation
1. Use LLMs to translate the newly generated logical expressions into natural language descriptions.
2. Combine the natural language descriptions of propositional symbols according to the extended logical expressions.
3. Incorporate the translated logical information as a new part of the original input prompt.
-- 4. Integration with Existing Prompting Methods
1. Combine the LoT-generated logical information with the original prompt.
2. Use this enhanced prompt with existing prompting methods like Chain-of-Thought (CoT), Self-Consistency (SC), or Tree-of-Thoughts (ToT).
3. Feed the augmented prompt to the LLM to generate the final answer.
What do you think about LoT?
- 1 reply
updated
a
collection
about 2 months ago
updated
a
collection
2 months ago
Reacted to
MohamedRashad's
post with ๐
2 months ago
Post
964
Qwen2.5-72B + Flux-dev + FinePersonas = Grounded Structured Character Generator
Check out my latest projects that uses Qwen/Qwen2.5-72B-Instruct , black-forest-labs/FLUX.1-dev , and MohamedRashad/FinePersonas-Lite to generate different characters in a world of your description.
Try Here: MohamedRashad/Character-Generator ๐ค
Check out my latest projects that uses Qwen/Qwen2.5-72B-Instruct , black-forest-labs/FLUX.1-dev , and MohamedRashad/FinePersonas-Lite to generate different characters in a world of your description.
Try Here: MohamedRashad/Character-Generator ๐ค
Reacted to
Wauplin's
post with ๐
2 months ago
Post
4511
๐ Exciting News! ๐
We've just released ๐๐๐๐๐๐๐๐๐๐๐_๐๐๐ v0.25.0 and it's packed with powerful new features and improvements!
โจ ๐ง๐ผ๐ฝ ๐๐ถ๐ด๐ต๐น๐ถ๐ด๐ต๐๐:
โข ๐ ๐จ๐ฝ๐น๐ผ๐ฎ๐ฑ ๐น๐ฎ๐ฟ๐ด๐ฒ ๐ณ๐ผ๐น๐ฑ๐ฒ๐ฟ๐ with ease using
โข ๐ ๐ฆ๐ฒ๐ฎ๐ฟ๐ฐ๐ต ๐๐ฃ๐: new search filters (gated status, inference status) and fetch trending score.
โข โก๐๐ป๐ณ๐ฒ๐ฟ๐ฒ๐ป๐ฐ๐ฒ๐๐น๐ถ๐ฒ๐ป๐: major improvements simplifying chat completions and handling async tasks better.
Weโve also introduced tons of bug fixes and quality-of-life improvements - thanks to the awesome contributions from our community! ๐ช
๐ก Check out the release notes: Wauplin/huggingface_hub#8
Want to try it out? Install the release with:
We've just released ๐๐๐๐๐๐๐๐๐๐๐_๐๐๐ v0.25.0 and it's packed with powerful new features and improvements!
โจ ๐ง๐ผ๐ฝ ๐๐ถ๐ด๐ต๐น๐ถ๐ด๐ต๐๐:
โข ๐ ๐จ๐ฝ๐น๐ผ๐ฎ๐ฑ ๐น๐ฎ๐ฟ๐ด๐ฒ ๐ณ๐ผ๐น๐ฑ๐ฒ๐ฟ๐ with ease using
huggingface-cli upload-large-folder. Designed for your massive models and datasets. Much recommended if you struggle to upload your Llama 70B fine-tuned model ๐คกโข ๐ ๐ฆ๐ฒ๐ฎ๐ฟ๐ฐ๐ต ๐๐ฃ๐: new search filters (gated status, inference status) and fetch trending score.
โข โก๐๐ป๐ณ๐ฒ๐ฟ๐ฒ๐ป๐ฐ๐ฒ๐๐น๐ถ๐ฒ๐ป๐: major improvements simplifying chat completions and handling async tasks better.
Weโve also introduced tons of bug fixes and quality-of-life improvements - thanks to the awesome contributions from our community! ๐ช
๐ก Check out the release notes: Wauplin/huggingface_hub#8
Want to try it out? Install the release with:
pip install huggingface_hub==0.25.0
- 1 reply
Reacted to
aaditya's
post with ๐
3 months ago
Post
3483
Last Week in Medical AI: Top Research Papers/Models
๐ (September 1 - September 7, 2024)
Medical LLM & Other Models :
- CancerLLM: Large Language Model in Cancer Domain
- MedUnA: Vision-Language Models for Medical Image
- Foundation Model for Robotic Endoscopic Surgery
- Med-MoE: MoE for Medical Vision-Language Models
- CanvOI: Foundation Model for Oncology
- UniUSNet: Ultrasound Disease Prediction
- DHIN: Decentralized Health Intelligence Network
Medical Benchmarks and Evaluations:
- TrialBench: Clinical Trial Datasets & Benchmark
- LLMs for Medical Q&A Evaluation
- MedFuzz: Exploring Robustness Medical LLMs
- MedS-Bench: Evaluating LLMs in Clinical Tasks
- DiversityMedQA: Assessing LLM Bias in Diagnosis
- LLM Performance in Gastroenterology
LLM Digital Twins:
- Digital Twins for Rare Gynecological Tumors
- DT-GPT: Digital Twins for Patient Health Forecasting
Medical LLM Applications:
- HIPPO: Explainable AI for Pathology
- LLMs vs Humans in CBT Therapy
- ASD-Chat: LLMs for Autistic Children
- LLMs for Mental Health
- LLMs for Postoperative Risk Prediction
Frameworks and Methodologies:
- Rx Strategist: LLM-based Prescription Verification
- Medical Confidence Elicitation
- Guardrails for Medical LLMs
Check the full thread : https://x.com/OpenlifesciAI/status/1832476252260712788
Thank you for your continued support and love for this series! Stay up-to-date with weekly updates on Medical LLMs, datasets, and top research papers by following @aaditya ๐ค
๐ (September 1 - September 7, 2024)
Medical LLM & Other Models :
- CancerLLM: Large Language Model in Cancer Domain
- MedUnA: Vision-Language Models for Medical Image
- Foundation Model for Robotic Endoscopic Surgery
- Med-MoE: MoE for Medical Vision-Language Models
- CanvOI: Foundation Model for Oncology
- UniUSNet: Ultrasound Disease Prediction
- DHIN: Decentralized Health Intelligence Network
Medical Benchmarks and Evaluations:
- TrialBench: Clinical Trial Datasets & Benchmark
- LLMs for Medical Q&A Evaluation
- MedFuzz: Exploring Robustness Medical LLMs
- MedS-Bench: Evaluating LLMs in Clinical Tasks
- DiversityMedQA: Assessing LLM Bias in Diagnosis
- LLM Performance in Gastroenterology
LLM Digital Twins:
- Digital Twins for Rare Gynecological Tumors
- DT-GPT: Digital Twins for Patient Health Forecasting
Medical LLM Applications:
- HIPPO: Explainable AI for Pathology
- LLMs vs Humans in CBT Therapy
- ASD-Chat: LLMs for Autistic Children
- LLMs for Mental Health
- LLMs for Postoperative Risk Prediction
Frameworks and Methodologies:
- Rx Strategist: LLM-based Prescription Verification
- Medical Confidence Elicitation
- Guardrails for Medical LLMs
Check the full thread : https://x.com/OpenlifesciAI/status/1832476252260712788
Thank you for your continued support and love for this series! Stay up-to-date with weekly updates on Medical LLMs, datasets, and top research papers by following @aaditya ๐ค