d
WE ARE LINGO
2nd floor, Garuda BHIVE, BMTC Complex,
Old Madiwala, Kuvempu Nagar,
Stage 2, BTM Layout, Bengaluru,
Karnataka – 560068. India.
Introducing HaluMon

Ensuring Language Model Reliability

At SandLogic, we are proud to present HaluMon, our innovative tool designed to monitor and control hallucinations in AI-generated responses. Inspired by the legendary Hanuman, HaluMon embodies strength and precision, ensuring that the outputs of our AI models are accurate, reliable, and trustworthy

Key Features

Real-Time Monitoring

Continuously checks AI outputs for potential hallucinations, ensuring immediate detection and correction.

Post-Processing Validation

Uses advanced algorithms to validate the accuracy of generated content against known data sources.

Human-in-the-Loop Mechanisms

Incorporates human oversight to verify critical outputs, combining AI efficiency with human judgment.

Context Awareness

Maintains context throughout interactions, reducing the risk of generating irrelevant or incorrect information.

Seamless Integration

Easily integrates with Lingo and LingoForge, enhancing their capabilities by ensuring the reliability of generated insights.

The Hallucination Handling Algorithm

The hallucination handling algorithm by SandLogic aims to detect hallucinated text generated by language models by leveraging a combination of linguistic processing and contextual analysis. The algorithm preprocesses the text to ensure uniformity in analysis. It then evaluates the text’s structure and content, considering factors such as context and generated token length to establish adaptive criteria for identifying anomalies. By observing contextual patterns, the algorithm can flag instances of hallucination, characterized by inconsistencies or unnatural repetitions. This methodology helps in distinguishing genuine, contextually appropriate text from spurious or fabricated content, enhancing the reliability of language model outputs.

Challenges in Controlling Hallucinations During Deployment

Deploying Specialized Language Models (SLMs) and Task-specific Language Models (TLMs) at customer premises comes with unique challenges:

  • Contextual Understanding: Ensuring the model accurately maintains context over long interactions to prevent irrelevant or fabricated content.
  • Complexity of Domain-Specific Language: Handling intricate terminologies and contexts in specific domains, which can lead to hallucinations if not properly managed.
  • Resource Constraints: Implementing sophisticated hallucination detection algorithms on-premises requires significant computational resources, which can be challenging for some deployments.
  • Real-Time Validation: Developing robust mechanisms for real-time detection and mitigation of hallucinations during live use, which is complex and resource-intensive.
  • Adaptability: Ensuring the model adapts to the dynamic data environments at the customer premises without generating hallucinations.
  • User Feedback Integration: Effectively incorporating user feedback to continually refine and reduce hallucinations in the deployed model.

Real-World Applications of HaluMon

HaluMon was leveraged by LingoForge in evaluating and monitoring each of the domain-specific training and fine-tuning of the SLMs mentioned below. HaluMon ensures the reliability and accuracy of AI-generated outputs, reducing hallucinations, and enhancing the overall effectiveness of these solutions. By integrating HaluMon, SandLogic’s models deliver high-performance, contextually appropriate, and trustworthy responses, driving better business outcomes. HaluMon can also be used to monitor any open-source Language Models.

Developed for a top 5 pharma equipment manufacturer, this Co-Pilot assists executives by integrating with their systems to provide real-time insights, automate tasks, and enhance data management. It enables seamless access to critical sales data directly from sources, facilitating informed decision-making.

This system aids agents in addressing customer queries during inbound calls by performing ASR, prompt generation based on the ASR transcript, and response generation using SLMs, all within 63 milliseconds, ensuring accurate and efficient service.

Post-call, the system uses ASR and SLMs to automatically triage issues reported by customers, streamlining the support process and improving response times.

Implemented for an outbound call center, this model scores buyer propensity with a classification accuracy of 98%, aiding the sales team in effectively targeting high-potential leads.

Why SanLogic’s LingoForge?

  • Domain Understanding: Leverages dense sets of volumetric data for deep domain knowledge, enabling precise and contextual AI responses.
  • Correlation & Reasoning Capability: Advanced algorithms enhance correlation and reasoning, providing insightful and logical outputs.
  • Performant Inference: Offers scalable and real-time inference, ensuring efficient processing and quick responses.
  • Reduced Training Time & Fast Inference: Optimized models significantly reduce training time and offer rapid inference, leading to operational efficiency.
  • Data Security: Ensures robust data security, protecting sensitive information and maintaining compliance with industry standards.