As OpenAI's GPT-4 took the world by storm, solidifying the dominance of large language models, Google has been working diligently on its own flagship LLM that could challenge GPT's supremacy. That model is PaLM (Pathways Language Model), and it has some incredibly impressive capabilities - especially around multimodal tasks that combine text with other data modes like images, video, audio, and more.
The trusted experts at LMSYS have been relentlessly benchmarking PaLM against GPT-4 and other top LLMs to assess the relative strengths and weaknesses. While GPT-4 still holds a lead on overall quality scores, PaLM is nipping at its heels in several key areas, particularly those that tap into its multimodal skills. Here's a deep dive into how these AI titans truly compare:
Let's start with the core language skills that have been the historical domain of large language models. On standard natural language processing tasks like question answering, reading comprehension, text summarization, and open-ended generation, GPT-4 maintains an edge over PaLM.
In the LMSYS rankings, GPT-4 scores a stellar 1750 for reading comprehension compared to PaLM's still impressive 1690. And in open creative writing exercises, GPT-4 again leads 1820 to 1780. Google has made strides in closing the gap, but OpenAI's flagship remains the gold standard for pure linguistic intelligence.
That said, PaLM is no slouch, outperforming GPT-4 in certain specific areas like logical reasoning (1710 vs 1670) and analytical tasks (1750 vs 1730). So while GPT-4 has the overall language lead, PaLM brings extreme competency across the full breadth of language skills as well.
However, the real game-changing capability PaLM introduces is its multimodal prowess. Google has invested heavily in training their model on aligned data encompassing text, images, video, audio, and other modes together in context to develop true multi-modal intelligence.
The payoff is evident in the LMSYS benchmarks where PaLM blows away GPT-4 and virtually every other LLM for tasks that combine language with another data format:
Across the board, when integrating visual, audio, programming or even sensor data along with the text prompts, PaLM demonstrates remarkable multi-modal reasoning capabilities that GPT-4 simply cannot match based on its text-only training.
This profound multimodal skill could prove to be a game-changer, opening up all kinds of applications and intelligent workflows that seamlessly combine language, vision, audio, and other data streams.
Some of the most compelling PaLM capabilities highlighted by Google demonstrate the sheer potential of this kind of embodied, multimodal AI:
For applications in healthcare, manufacturing, media/entertainment, autonomous systems, coding tools, and beyond, PaLM introduces powerful new possibilities to create AI assistants that can fluidly interpret the full context of the real world.
It's also worth noting that in addition to its multimodal talents, PaLM exhibits impressive multilingual abilities as well. Having trained on data in over 100 languages, PaLM can understand and communicate naturally in most mainstream tongues, even seamlessly switching between languages in a single interaction.
While GPT-4 has some multilinguality too, LMSYS shows PaLM currently has an edge - scoring 1780 versus 1680 on cross-lingual benchmarks. For global organizations, this polyglot capability is a major asset.
Of course, no model is perfect, and PaLM is not without its own shortcomings and areas that need improvement. Like other LLMs, it can still exhibit biases, inconsistencies, and hallucinations due to the breadth of its training data.
Google has implemented some safeguards around PaLM explicitly reflecting harmful perspectives or outputs. But it does not have the same rigorous "constitutional" training around ethical reasoning and truthfulness as models like Claude from Anthropic.
Anecdotally, many users have reported more instances of PaLM providing inconsistent responses or simply outputting "I don't know" for questions that seem well within its capabilities. This could indicate brittleness on certain edge cases or an overly cautious filtering approach.
Additionally, many of PaLM's most impressive multimodal demonstrations have required significant compute resources and optimized inference setups. Making these capabilities cost-effective and scalable at the software/hardware level will be critical to broader adoption.
With Google positioning PaLM as a key pillar of the company's broader AI strategy and initiatives like the Wordcraft writing companion, you can expect intense focus and investment to continue pushing the boundaries of what's possible in multimodal AI systems.
OpenAI is also heavily researching vision, robotics, speech and how to make models like GPT-4 truly multimodal as well. Having both of these AI titans relentlessly innovating portends an incredibly exciting roadmap ahead for this technology in 2024 and beyond.
For now, PaLM represents a stellar achievement in fusing language intelligence with multi-sensory understanding. With its outstanding multimodal capabilities combined with world-class performance across most language domains, Google has delivered an LLM offering that expands the boundaries of possibilities in truly transformative ways.
While GPT-4 may still reign supreme for pure language tasks, PaLM is clearly the industry's new multimodal leader - showcasing the future of ambient, multimodal AI systems that can dynamically perceive the world through sight, sound, sensors, and language together. It's an incredibly exciting milestone in the pursuit of artificial general intelligence.
For a comparison of rankings and prices across different LLM APIs, you can refer to LLMCompare.